Dysregulated Endogenous Opioid Signalling and Reward Processing in Alcohol Dependence

Dysregulated endogenous opioid signalling and reward processing in alcohol dependence

Dr Samuel Turton

Imperial College London Department of Medicine

A thesis submitted for the degree of Doctor of Philosophy

November 2018

1 DECLARATION OF ORIGINALITY

All the work presented in this thesis is my own except where the work of others is referenced in the text of this thesis.

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2 ABSTRACT

Alcohol dependence has been estimated to affect 5.5.% of adults in Europe and has a significant impact on an individual’s physical and mental health and also substantial costs to wider society. Current treatments for alcohol dependence, such as psychosocial interventions and pharmacotherapy, have limited success. Most individuals relapse to alcohol use within a year and this reflects a substantial unmet need.

Addiction has been described as a ‘reward deficient’ state and there is evidence that the endogenous opioid system, which plays an important role in reward, is dysregulated in alcohol dependence and other addictions. This thesis aimed to characterise the endogenous opioid system in abstinent alcohol dependent participants using [11C]carfentanil, a selective mu-opioid receptor (MOR) agonist positron emission tomography (PET) radioligand, and an oral 0.5mg/kg dexamphetamine challenge to examine MOR availability and endogenous opioid release. Furthermore, the associations between reward responses, measured as financial reward anticipation during a monetary incentive delay (MID) task functional magnetic resonance imaging (fMRI) paradigm, MOR availability and endogenous opioid release in healthy controls, alcohol dependent and gambling disorder participants were also examined and compared.

Abstinent alcohol dependent participants did not show any differences in MOR availability compared with healthy controls, but there was evidence of blunted dexamphetamine- induced endogenous opioid release. There were associations between MID task financial reward anticipation responses and MOR availability and dexamphetamine-induced endogenous opioid release in both alcohol dependent and gambling disorder participants.

The results suggest that low endogenous opioid tone may be an important factor in alcohol dependence and other addictions. There is also evidence of a link between dysregulated endogenous opioid signalling and dysregulated reward responses in addiction. Further research is required to understand how dysregulated endogenous opioid signalling is associated with relapse risk, and which treatments might be most effective in mediating dysregulated endogenous opioid signalling and reward sensitivity.

3 CONTENTS

ABSTRACT ...... 3 ACKNOWLEDGEMENTS ...... 24 LIST OF ABBREVIATIONS ...... 27

1. INTRODUCTION ...... 31 1.1. Alcohol dependence ...... 31 1.1.1. The pharmacology of alcohol ...... 32 1.1.2. Treatment of alcohol dependence ...... 32 1.1.3. Opioid receptor antagonists in alcohol dependence treatment ...... 33 1.2. Gambling Disorder ...... 34 1.3. Endogenous opioid signalling in alcohol dependence ...... 35 and other addictions. 1.3.1. The Mu-opioid receptor (MOR) ...... 35 1.3.2. MORs and mesocorticolimbic dopamine signalling ...... 36 1.3.3. Substances of abuse, reward and MORs ...... 36 1.3.4. MORs, endogenous opioid signalling and addiction ...... 37 1.4. Positron Emission Tomography (PET) imaging ...... 38 1.4.1. Overview of PET imaging ...... 38 1.4.2. Overview of PET kinetic modelling ...... 39 1.4.3. Compartmental modelling in PET ...... 40 1.4.4. Reference tissue models in PET ...... 42 1.4.5. Imaging neurotransmitter release with PET ...... 43 1.5. Imaging MORs with PET and [11C]carfentanil ...... 43 1.5.1. Investigating differences in MOR availability in humans with ...... 45 [11C]carfentanil PET 1.5.2. Imaging MOR in alcohol dependence and other addictions ...... 45 with [11C]carfentanil PET 1.5.3. Measuring endogenous opioid release with [11C]carfentanil PET ...... 50

4 1.5.4. [11C]carfentanil PET and dexamphetamine challenge-induced ...... 51 endogenous opioid release 1.5.5. [11C]carfentanil and dexamphetamine challenge in ...... 53 gambling disorder 1.6. The MOR OPRM1 A118G polymorphism ...... 53 1.6.1. In vitro functional effects of OPRM1 A118G polymorphism ...... 54 1.6.2. OPRM1 A118G polymorphism and alcohol dependence ...... 54 1.6.3. The OPRM1 A118G polymorphism and [11C]carfentanil PET ...... 56 1.6.4. OPRM1 A118G polymorphism and functional MRI ...... 56 brain responses 1.7. Functional Magnetic Resonance Imaging (fMRI) ...... 57 1.7.1. Blood Oxygen Level Dependent (BOLD) fMRI ...... 57 1.7.2. Pre-processing of task-based fMRI data ...... 58 1.7.3. Modelling of task-based fMRI data ...... 59 1.8. Monetary Incentive Delay (MID) Task ...... 60 1.8.1. The ICCAM platform MID task ...... 61 1.8.2. MID task in alcohol dependence ...... 62 1.8.3. MID task in gambling disorder ...... 64 1.9. Combining PET and fMRI imaging ...... 65 1.9.1. Simultaneous PET/fMRI imaging ...... 67 1.10. Aims of this thesis ...... 68

2. METHODS ...... 71 2.1. Statement of contribution to the work in this thesis ...... 72 2.1.1. Data collection ...... 72 2.1.2. Hypothesis development ...... 73 2.1.3. Data analysis ...... 74 2.1.4. Results interpretation ...... 75 2.2. Study populations ...... 76 2.2.1. [11C]carfentanil PET in alcohol dependence ...... 76 2.2.2. [11C]-Ro15 4513 PET in alcohol dependence ...... 77 2.2.3. [11C]carfentanil PET in healthy controls ...... 77

5 2.2.4. [11C]carfentanil PET in gambling disorder ...... 78 2.2.5. [11C]-Ro15 4513 PET in gambling disorder ...... 78 2.3. Eligibility criteria ...... 79 2.3.1. General eligibility criteria ...... 79 2.3.2. Additional healthy control eligibility criteria ...... 80 2.3.3. Additional alcohol dependent participants eligibility criteria ...... 80 2.3.4. Additional gambling disorder participant eligibility criteria ...... 81 2.3.5. Additional comments regarding eligibility criteria ...... 81 2.4. Recruitment ...... 82 2.5. Study visits ...... 82 2.5.1. Screening visit ...... 82 2.5.2. [11C]carfentanil PET scan visit ...... 83 2.6. Study questionnaires ...... 84 2.6.1. Beck Depression Inventory II (BDI) ...... 84 2.6.2. Spielberger Trait Anxiety Scale (STAI) and Spielberger ...... 85 State Anxiety Scale (SSAI) 2.6.3. UPPS-P Impulsivity Scale ...... 85 2.6.4. Barrett Impulsivity Scale questionnaire-11 (BIS) ...... 86 2.6.5. Fagerström Test for Nicotine Dependence (FTND) ...... 86 2.6.6. Alcohol Urge Questionnaire (AUQ) ...... 86 2.6.7. Severity of Alcohol Dependence Questionnaire (SADQ) ...... 87 2.6.8. Time to Relapse Questionnaire (TRQ) ...... 87 2.6.9. Simplified Amphetamine Interview Rating Scale (SAIRS) ...... 87 2.7. Other clinical measures ...... 88 2.7.1. Harmful alcohol use and abstinence duration ...... 88 2.8. Blood sampling for cortisol, dexamphetamine and genotype data ...... 89 2.8.1. Plasma dexamphetamine concentration data ...... 89 2.8.2. Serum cortisol concentration data ...... 90 2.8.3. OPRM1 DNA sampling and analysis ...... 91 2.9. Data analysis ...... 92 2.9.1. Software packages ...... 92 2.9.2. Statistical analyses ...... 92

6 2.9.3. Corrections for multiple comparisons ...... 93 2.10. [11C]carfentanil PET Imaging data ...... 94 2.10.1. [11C]carfentanil PET data collection ...... 94 2.10.2. [11C]carfentanil PET data processing ...... 94 2.10.3. Non-linear co-registration methods in[11C]carfentanil PET analysis ... 96 2.10.4. ROI volume in healthy controls and alcohol dependent ...... 100 participants 2.10.5. Different day pre- and post-dexamphetamine PET scans ...... 102 2.10.6. Examination of intra-scan head motion before and ...... 103 after dexamphetamine challenge 2.11. Selecting regions of interest for [11C]carfentanil PET data analysis ...... 104 2.11.1. Selecting high [11C]Carfentanil binding regions ...... 104 2.11.2. Grey matter masks ...... 106 2.12. Magnetic resonance imaging (MRI) procedures ...... 108 2.12.1. MRI and fMRI data acquisition ...... 108 2.12.2. MRI and fMRI data pre-processing ...... 109 2.13. ICCAM MID task ...... 110 2.13.1. ICCAM MID task modelling ...... 111 2.13.2. Quality control of pre-processed MRI data and ...... 112 MID task modelling 2.14. ROIs for fMRI and combined PET and fMRI analyses ...... 113 2.14.1. MID ‘functional’ ROIs ...... 113

11 2.14.2. Using ROI or fROI [ C]Carfentanil BPND values for combined ...... 115 PET and fMRI analyses 2.15. Whole-brain FLAME analyses ...... 118 2.15.1. Comparing MID win>neutral anticipation BOLD ...... 118 contrast between groups 2.15.2. FSL FLAME models examining associations between ...... 118 MID win>neutral anticipation BOLD contrast and [11C]Carfentanil

BPND/∆BPND ROI values 2.15.3. Interactions between effects of status and [11C]Carfentanil ...... 119

BPND/∆BPND on MID win>neutral anticipation BOLD contrast

7 2.15.4. The effect of OPRM1 genotype on MID win>neutral ...... 120 anticipation BOLD contrast 2.15.5. Interactions between effects of OPRM1 genotype ...... 120

11 and [ C]Carfentanil BPND/∆BPND on MID win>neural anticipation BOLD contrast. 2.16. Power calculations ...... 121

3. INVESTIGATING MOR AVAILABILITY IN ABSTINENT ALCOHOL ...... 125 DEPENDENT PARTICIPANTS 3.1. Introduction ...... 125 3.1.1. Aims ...... 125 3.1.2. Introduction to MOR availability results chapter ...... 125 3.1.3. Hypotheses ...... 126 3.2. Methods ...... 127 3.3. Results ...... 127 3.3.1. Demographics ...... 127

11 3.3.2. Comparison of baseline [ C]carfentanil BPND between ...... 128 healthy controls and alcohol dependent participants 3.3.3. Associations between MOR availability and duration of ...... 130 abstinence from alcohol 3.3.4. Associations between MOR availability and lifetime high ...... 131 risk alcohol exposure 3.3.5. Associations between MOR availability and SADQ scores ...... 131 3.3.6. Associations between MOR availability and TRQ scores ...... 131 3.3.7. Alcohol Urge Questionnaire scores ...... 132 3.3.8. Associations between MOR availability and ...... 132 UPPS-P Impulsivity scores 3.3.9. Associations between MOR availability, BDI, STAI and SSAI scores .... 132 3.3.10. Examining the potential confounding effect of ...... 133

11 smoking and nicotine dependence on [ C]carfentanil BPND 3.3.11. Examining the associations between age and MOR availability ...... 136

8 3.3.12. Examining the potential confounding effect of the ...... 137 OPRM1 A118G polymorphism on MOR availability 3.3.13. Examining the potential confounding effect of cold carfentanil ...... 139 mass on MOR availability 3.4. Discussion ...... 141

11 3.4.1. Comparisons of [ C]carfentanil BPND between healthy controls ...... 141 and alcohol dependent participants 3.4.2. Associations between MOR availability and clinical variables ...... 144 associated with alcohol use and dependence 3.4.3. Associations between MOR availability and other clinical ...... 145 variables of interest 3.4.4. Examining the potential confounding effect of current ...... 146 smoking and nicotine dependence on MOR availability 3.4.5. Examining the potential confounding effect of differences ...... 147 in age on MOR availability 3.4.6. The effects of the OPRM1 A118G polymorphism on ...... 148 MOR availability

11 3.4.7. The effect of cold carfentanil mass on [ C]carfentanil BPND ...... 149 3.4.8. Limitations ...... 150 3.5. Conclusion ...... 156

4. INVESTIGATING ENDOGENOUS OPIOID TONE IN ALCOHOL DEPENDENCE ...... 157 4.1. Introduction ...... 157 4.1.1. Aims ...... 157 4.1.2. Introduction to endogenous opioid tone results chapter ...... 157 4.1.3. Hypotheses ...... 159 4.2. Methods ...... 159 4.2.1. Study population and scanning procedures ...... 159

11 4.2.2. Calculating [ C]carfentanil ∆BPND ...... 160 4.3. Results ...... 160 4.3.1. Demographics ...... 160

9 11 4.3.2. Changes in [ C]carfentanil BPND following 0.5mg/kg oral ...... 163 dexamphetamine challenge in healthy controls

11 4.3.3. Changes in [ C]carfentanil BPND following 0.5mg/kg oral ...... 164 dexamphetamine challenge in alcohol dependent participants

11 4.3.4. Changes in [ C]carfentanil BPND following 0.5mg/kg oral ...... 166 dexamphetamine challenge compared between healthy controls and alcohol dependent participants

11 4.3.5. Changes in [ C]carfentanil BPND following 0.5mg/kg oral ...... 169 dexamphetamine challenge compared between alcohol dependent and gambling disorder participants. 4.3.6. Plasma dexamphetamine pharmacokinetics ...... 171 4.3.7. Associations between plasma dexamphetamine ...... 173

11 concentrations and [ C]carfentanil ∆BPND 4.3.8. Individual serum cortisol responses to the oral ...... 175 dexamphetamine challenge 4.3.9. Examining possible ‘high’ and ‘low’ cortisol responses in ...... 176 alcohol dependent participants 4.3.10. Comparing serum cortisol concentrations between healthy ...... 178 controls and alcohol dependent participants 4.3.11. Subjective effects of oral dexamphetamine challenge ...... 180 in healthy controls 4.3.12. Subjective effects of oral dexamphetamine challenge ...... 182 in alcohol dependent participants 4.3.13. Comparing SAIRS scores between healthy controls and ...... 184 alcohol dependent participants 4.3.14. Associations between change in SAIRS scores from baseline ...... 187

11 and [ C]carfentanil ∆BPND

11 4.3.15. Associations between [ C]Carfentanil ∆BPND and measures ...... 188 related to alcohol use and dependence

11 4.3.16. Associations between [ C]carfentanil ∆BPND and other clinical ...... 189 variables

10 4.3.17. Examining the potential confounding effect of current smoking ...... 190

11 and nicotine dependence on [ C]carfentanil ∆BPND 4.3.18. Examining the potential confounding effects of the OPRM1 ...... 193

11 A118G polymorphism on [ C]carfentanil ∆BPND 4.4. Discussion ...... 195 4.4.1. Oral dexamphetamine-induced endogenous opioid release ...... 196 in healthy controls 4.4.2. Blunted dexamphetamine-induced endogenous opioid ...... 198 release in abstinent alcohol dependent participants 4.4.3. Dysregulated salience responses in addiction as a mechanism ...... 199 for blunted oral dexamphetamine-induced endogenous opioid release

11 4.4.4. Comparison of [ C]carfentanil ∆BPND between alcohol ...... 200 dependence and gambling disorder 4.4.5. Plasma dexamphetamine concentrations in healthy controls ...... 201 and alcohol dependent participants 4.4.6. Serum cortisol concentrations in healthy controls ...... 203 and alcohol dependent participants 4.4.7. Measuring subjective responses to the oral dexamphetamine ...... 205 challenge with SAIRS scores 4.4.8. Associations between endogenous opioid release, duration ...... 206 of abstinence and harmful alcohol consumption 4.4.9. Associations between endogenous opioid release, ...... 207 SADQ and TRQ scores 4.4.10. Associations between BDI scores and oral dexamphetamine- ...... 208 induced endogenous opioid release 4.4.11. Associations between UPPS-P impulsivity scale scores and ...... 209 oral dexamphetamine-induced endogenous opioid release 4.4.12. Current smoking and nicotine dependence as potential ...... 209

11 confounders of oral dexamphetamine-induced [ C]carfentanil ∆BPND 4.4.13. Effects of the OPRM1 A118G polymorphism on endogenous ...... 210 opioid release 4.4.14. Limitations ...... 210

11 4.5. Conclusion ...... 214

5. COMBINING [11C]CARFENTANIL PET AND MONETARY INCENTIVE ...... 215 DELAY FMRI TO EXAMINE THE ASSOCIATIONS BETWEEN OPIOIDERGIC SIGNALLING AND REWARD RESPONSES IN ADDICTION. 5.1. Introduction ...... 215 5.1.1. Aims ...... 215 5.1.2. Introduction to combined [11C]carfentanil PET and fMRI ...... 215 5.1.3. Hypotheses ...... 217 5.2. Methods ...... 218 5.2.1. Study sample ...... 218 5.2.2. Graphical representation of whole brain analysis results ...... 204 5.3. Results ...... 220 5.3.1. MID fMRI dataset demographics ...... 220 5.3.2. fMRI MID dataset task behavioural measures ...... 222 5.3.3. Group comparison of MID win>neutral anticipation ...... 223 BOLD contrast 5.3.4. Combined MID fMRI and [11C]carfentanil PET dataset ...... 224 demographics 5.3.5. Combined [11C]carfentanil PET and MID fMRI dataset task ...... 226 behavioural measures 5.3.6. Combined MID win>neutral anticipation BOLD contrast and ...... 226

11 baseline [ C]carfentanil BPND – ROI analysis. 5.3.7. Combined MID win>neutral anticipation BOLD contrast and ...... 228

11 baseline [ C]carfentanil BPND – FSL FLAME analysis 5.3.8. Examining group differences in correlations between MID ...... 230 win>neutral anticipation BOLD contrast and baseline

11 [ C]carfentanil BPND 5.3.9. Summary of results from FSL FLAME analyses examining ...... 232 correlations between MID win>neutral anticipation BOLD

11 contrast and baseline [ C]carfentanil BPND

12 5.3.10. Combined MID win>neutral anticipation BOLD contrast and ...... 233

11 dexamphetamine-induced [ C]carfentanil ∆BPND – ROI analysis 5.3.11. Combined MID win>neutral anticipation BOLD contrast ...... 234

11 and dexamphetamine-induced [ C]carfentanil ∆BPND – FSL FLAME analyses 5.3.12. Examining group differences in correlations between MID ...... 236 win>neutral anticipation BOLD contrast and baseline

11 [ C]carfentanil ∆BPND 5.3.13. Summary of results from FSL FLAME analyses examining ...... 238 correlations between MID BOLD win>neutral anticipation

11 and oral dexamphetamine-induced [ C]carfentanil ∆BPND 5.3.14. Examining the potential confounding effects of the OPRM1 ...... 238 polymorphism on MID win>neutral anticipation BOLD contrast – ROI analyses 5.3.15. Examining the potential confounding effects of the OPRM1 ...... 239 polymorphism on MID win>neutral anticipation BOLD contrast – FSL FLAME analyses 5.3.16. Examining the potential confounding effects of the OPRM1 ...... 240

11 genotype on correlations between [ C]carfentanil BPND/∆BPND and MID win>neutral anticipation BOLD contrast – FSL FLAME analyses. 5.4. Discussion ...... 241 5.4.1. MID win>neutral anticipation BOLD contrast compared ...... 242 between groups 5.4.2. Negative correlations between MID win>neutral anticipation ...... 244 BOLD contrast and putamen MOR availability in alcohol dependence 5.4.3. Putamen MOR signalling in alcohol dependence ...... 247 5.4.4. Negative correlations between MID win>neutral anticipation ...... 248 BOLD contrast and oral dexamphetamine-induced endogenous opioid release in gambling disorder participants 5.4.5. Associations between oral dexamphetamine-induced ...... 250 endogenous opioid release and MID win>neutral anticipation BOLD

13 contrast – Comparisons between alcohol dependence and gambling disorder 5.4.6. The potential confounding effects of differences in the ...... 252 MID task behavioural measures between groups on win>neutral anticipation BOLD contrast 5.4.7. The OPRM1 polymorphism and MID win>neutral anticipation ...... 253 BOLD contrast in healthy controls 5.4.8. The potential mediating effects of the OPRM1 polymorphism ...... 254 on associations between MID win>neutral anticipation BOLD

11 contrast and [ C]carfentanil PET measures (BPND/∆BPND). 5.4.9. Limitations ...... 255 5.5. Conclusion ...... 263

6. GENERAL DISCUSSION ...... 265 6.1. Long-term abstinent alcohol dependent individuals do not have ...... 265 higher MOR availability 6.2. Oral dexamphetamine-induced endogenous opioid release is ...... 266 blunted in alcohol dependent participants 6.3. Associations between financial reward anticipation responses, ...... 267 MOR availability and endogenous opioid release 6.4. Comparisons between alcohol dependence and gambling disorder ...... 269 6.5. Abstinence and relapse risk ...... 270 6.6. Potential clinical implications of these results ...... 272 6.6.1. Opioid receptor agonist treatment in alcohol dependence ...... 272 6.6.2. Low endogenous opioid tone in alcohol dependence ...... 274 6.7. Further research ...... 275 6.7.1. Exploring endogenous opioid tone with [11C]carfentanil ...... 275 PET in individuals at high-risk of developing alcohol dependence 6.7.2. Exploring blunted endogenous opioid release in other addictions ..... 276 6.7.3. Exploring the relationship between low endogenous opioid ...... 276 tone and risk of relapse

14 6.7.4. Examining associations between oral dexamphetamine ...... 277

11 challenge-induced reductions in [ C]carfentanil BPND and changes in plasma β-endorphin concentrations 6.7.5. Examining the association between salience and blunted ...... 278 endogenous opioid release. 6.7.6. Exploring blunted endogenous opioid release in female ...... 279 alcohol dependent individuals 6.7.7. Exploring endogenous opioid release during the MID task and ...... 279 the effect of opioid signalling on MID task win>neutral anticipation BOLD contrast 6.8. Conclusions ...... 280

7. REFERENCES ...... 283 8. APPENDIX ...... 317

15 LIST OF FIGURES

1.1. Representation of the modulation of mesocorticolimbic dopaminergic ...... 36 activity by MOR receptors. 1.2. Example of a one-tissue compartment PET model ...... 40 1.3. Example of a three-tissue compartment model ...... 41 1.4. Example of a two-tissue compartment model ...... 41 1.5. Example of a simplified reference tissue model (SRTM) ...... 42 1.6. Example of vowel-wise [11C]carfentanil binding in the human brain ...... 44

2.1. Representation of the [11C]carfentanil PET and dexamphetamine challenge ...... 84 study visit completed by healthy controls, alcohol dependent and gambling disorder participants. 2.2. Collection timings of plasma dexamphetamine and serum cortisol samples ...... 91 2.3. Eroded occipital lobe white matter mask used as reference region for ...... 96 [11C]carfentanil SRTM model. 2.4. Non-linear co-registration of MNI 152 template to subject space comparing ...... 100 between MNI SPM8 Normalisation and SPM12 Unified Segmentation. 2.5. Volumes (mm3) of 4 ROIs in 32 healthy controls and 13 alcohol ...... 101 dependent participants

11 2.6. Individual NAcc [ C]carfentanil ∆BPND in Healthy control and alcohol dependent ...... 102 participants who had their [11C]carfentanil PET scans on the same and different days. 2.7. Illustrated example of the hierarchical structure of the regions in the CIC atlas ...... 104 2.8. Cerebellum ROI mask overlaid on T1 image in MNI 152 space and mean ...... 107

11 [ C]Carfentanil BPND image in MNI 152 space. 2.9. ICCAM MID Task cues, target and feedback as seen by participants during the ...... 111 fMRI scan. 2.10. Mean MID win>neutral anticipation BOLD contrast in healthy controls from ...... 114 the ICCAM pilot study. 2.11. Example of differences between CIC atlas ‘structural ROI’ and ‘functional ROI’ ...... 114 created using ICCAM MID pilot data. 2.12. Functional ROIs used to extract MID win>neutral anticipation %BOLD signal change ...... 115 2.13. Association between structural Atlas ROI and functional ROI [11C]Carfentanil ...... 116

BPND values extracted from parametric maps.

16 11 2.14. NAcc [ C]Carfentanil BPND and ∆BPND association between whole TAC ROI ...... 117

values generated in MIAKAT, and extracted values from BPND parametric images using the structural ROI mask. 2.15. Example of FSL FLAME model examining correlations between PET and fMRI data ...... 119 2.16. Example FSL FLAME model examining the interaction between the effects of ...... 120

11 status and putamen [ C]carfentanil BPND on MID win>neutral anticipation BOLD contrast 2.17. Example FSL FLAME model examining the interaction between the effects of ...... 121

11 OPRM1 genotype and putamen [ C]carfentanil BPND on MID win>neutral anticipation BOLD contrast

11 3.1. Mean [ C]carfentanil BPND in healthy controls and alcohol dependent ...... 130

participants in 21 high-BPND ROIs.

11 3.2. Mean [ C]Carfentanil BPND in smoking and non-smoking healthy controls ...... 135 and smoking and non-smoking alcohol dependent participants.

11 3.3. Age and putamen [ C]carfentanil BPND in healthy control participants ...... 137

11 3.4. Mean [ C]carfentanil BPND in OPRM1 A:A and OPRM1 G-allele carriers ...... 139 in combined healthy control and alcohol dependent cohort.

11 3.5. Cold carfentanil mass and Amygdala [ C]carfentanil BPND in healthy controls ...... 140 3.6. Proposed hypothesis of opioid homeostasis in relation to alcohol use in alcohol ...... 142 dependence (adapted from Hermann et al. 2017).

11 4.1. Mean [ C]Carfentanil ∆BPND values in healthy controls and alcohol dependent ...... 169 participants across 21 high-binding ROIs.

11 4.2. Mean [ C]Carfentanil ∆BPND values in alcohol dependent and gambling disorder ...... 171 participants across 21 ROIs. 4.3. Mean plasma dexamphetamine concentrations in healthy controls ...... 173 and alcohol dependent participants 4.4. Individual serum cortisol concentrations in healthy controls ...... 175 4.5. Individual serum cortisol concentrations in alcohol dependent participants ...... 176 4.6. Mean serum cortisol concentrations in ‘high’ and ‘low’ cortisol responding ...... 177 alcohol dependent participants.

11 4.7. Mean [ C]Carfentanil ∆BPND values in ‘high’ and ‘low’ cortisol releasing ...... 178 alcohol dependent participants. 4.8. Mean serum cortisol concentrations in healthy controls, ‘high’- and ‘low’-cortisol ...... 180 responding alcohol dependent participants.

17 4.9. Mean SAIRS ‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’ scores in healthy controls ...... 182 4.10. Mean SAIRS ‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’ scores in alcohol ...... 184 dependent participants. 4.11. Mean change in SAIRS scale scores from baseline in healthy controls and ...... 187 alcohol dependent participants.

11 4.12. Associations between NAcc [ C]Carfentanil ∆BPND and change in SAIRS ...... 188 ‘Alert’ scores in healthy controls

11 4.13. Associations between amygdala [ C]carfentanil ∆BPND and BDI scores ...... 190 in alcohol dependent participants.

11 4.14. NAcc [ C]carfentanil ∆BPND in current non-smoking healthy controls and ...... 192 alcohol dependent participants and current smoking controls and alcohol dependent participants.

11 4.15. Mean [ C]carfentanil ∆BPND in healthy control OPRM1 A-allele homozygous ...... 194 and G-allele carrier participants.

11 4.16. Mean [ C]carfentanil ∆BPND in alcohol dependent OPRM1 A-allele homozygous ...... 195 and G-allele carrier participants. 4.17. A representation of the hypothalamic-pituitary–adrenal axis ...... 204

11 5.1. Example FSL FLAME model examining correlations between [ C]carfentanil BPND ...... 219 and MID win>neutral anticipation BOLD contrast. 5.2. Mean MID win>neutral anticipation %BOLD change in three participant groups ...... 223

11 5.3. Negative correlation between putamen [ C]carfentanil BPND and putamen ...... 228 win>neutral anticipation %BOLD signal change in alcohol dependent participants.

11 5.4. Negative correlation between ventral pallidum [ C]carfentanil BPND and putamen ...... 228 win>neutral anticipation %BOLD signal change in gambling disorder participants.

11 5.5. Negative correlations between putamen [ C]carfentanil BPND and MID ...... 229 win>neutral anticipation BOLD contrast in alcohol dependent participants.

11 5.6. Negative correlations between ventral pallidum [ C]carfentanil BPND and MID ...... 230 win>neutral anticipation BOLD contrast in gambling disorder participants.

11 5.7. Differences in correlation between putamen [ C]carfentanil BPND and MID ...... 231 win>neutral anticipation BOLD contrast in alcohol dependent participants compared with gambling disorder participants.

11 5.8. Correlations between putamen [ C]carfentanil BPND and MID win>neutral ...... 232 anticipation %BOLD change in alcohol dependent and gambling disorder participants.

11 5.9. Correlation between ventral pallidum [ C]carfentanil ∆BPND and ...... 234 caudate win>neutral anticipation %BOLD signal change in healthy controls.

18 11 5.10. Negative correlations between ventral pallidum [ C]carfentanil ∆BPND and ...... 235 MID win>neutral anticipation BOLD contrast in healthy controls.

11 5.11. Negative correlations between ventral pallidum [ C]carfentanil ∆BPND and ...... 236 MID win>neutral anticipation BOLD contrast in gambling disorder participants.

11 5.12. Differences in correlation between ventral pallidum [ C]carfentanil ∆BPND and ...... 237 MID win>neutral anticipation BOLD contrast in alcohol dependent participants compared with gambling disorder participants.

11 5.13. Correlations between ventral pallidum [ C]carfentanil ∆BPND and MID ...... 237 win>neutral anticipation %BOLD signal change in alcohol dependent participants and gambling disorder participants. 5.14. Mean NAcc MID win>neutral anticipation %BOLD change in A-allele ...... 239 homozygous and G-allele carriers. 5.15. Higher win>neutral anticipation BOLD contrast in G-allele carriers ...... 240 compared with A-allele homozygous individuals in healthy controls.

19 LIST OF TABLES

1.1. Summary of [11C]carfentanil PET studies in addiction ...... 48 1.2. Summary of published studies examining the MID task fMRI in alcohol dependence ...... 63 1.3. Summary table of published studies combining neuroreceptor ...... 66 PET and fMRI imaging.

2.1. Summary of the number of individual healthy control, alcohol dependent and ...... 79 gambling disorder participants across the five studies with [11C]carfentanil PET and ICCAM MID Task data. 2.2. Mixed model ANOVA examining the within-subject effects of ROI and Registration ...... 98

11 and between-subject effect of Status on [ C]Carfentanil BPND in healthy controls and alcohol dependent participants. 2.3. Repeated measures ANOVA examining the within-subject effects of ROI and ...... 98

11 Registration on [ C]Carfentanil BPND in healthy controls alcohol dependent participants.

11 2.4. Mean [ C]Carfentanil BPND in 10 ROIs comparing SPM8 Normalisation ...... 99 and SPM12 Unified Segmentation registration methods in healthy controls and alcohol dependent participants. 2.5. Mean volume of 10 ROIs in healthy controls and lcohol dependent participants ...... 101

11 2.6. Pearson’s R values for correlations between [ C]carfentantil BPND and ...... 102 volume in 10 ROIs in healthy controls and alcohol dependent participants. 2.7. Mean inter-frame head motion parameters during pre- and post-dexamphetamine ...... 103 [11C]carfentanil PET scans in healthy controls and alcohol dependent participants

11 2.8. List of 22 CIC atlas ROIs to be assessed for [ C]carfentanil BPND ...... 106

11 2.9. Mean [ C]Carfentanil BPND values in cortical regions using no mask or ...... 107 grey matter mask for ROI analysis in healthy controls.

3.1. Demographic measures compared between healthy controls and alcohol ...... 128 dependent participants. 3.2. Mixed model ANOVA examining the within-subject effect of ROI and between-subject ... 129

11 effect of Status on [ C]Carfentanil BPND in healthy controls and alcohol dependent participants.

11 3.3. Mean [ C]carfentanil BPND in healthy controls and alcohol dependent participants ...... 129 3.4. Mixed model ANOVA examining the within-subject effect of ROI and ...... 133

11 between-subject effect of smoking on [ C]Carfentanil BPND in healthy controls.

20 3.5. Mixed model ANOVA examining the within-subject effect of ROI and ...... 134

11 between-subject effect of Smoking on [ C]Carfentanil BPND in alcohol dependent participants. 3.6. Mixed model ANOVA examining the within-subject effect of ROI and ...... 134

11 between-subject effects of Smoking and Status on [ C]Carfentanil BPND in healthy controls and alcohol dependent participants. 3.7. Mixed model ANOVA examining the within-subject effect of ROI and ...... 136

11 between-subject effect of Age on [ C]Carfentanil BPND in healthy controls. 3.8. Mixed model ANOVA examining the within-subject effect of ROI and ...... 136

11 between-subject effect of Age on [ C]Carfentanil BPND in alcohol dependent participants. 3.9. Selected results from Pearson’s correlation coefficient examining correlations ...... 137

11 between Age and [ C]Carfentanil BPND in healthy controls. 3.10. Mixed-model ANOVA examining the within-subject effect of ROI and ...... 138

11 between-subject effects of Status and Genotype on [ C]Carfentanil BPND in healthy controls and alcohol dependent participants. 3.11. Mean injected cold carfentanil mass in three different healthy control participant ...... 140 groups and alcohol dependent participants.

4.1. Demographic measures compared between healthy controls, ...... 162 alcohol dependent and gambling disorder participants. 4.2. Repeated measures ANOVA examining the within-subject effects of Scan and ...... 163

11 ROI on [ C]carfentanil BPND in healthy controls.

11 4.3. Mean pre- and post-dexamphetamine [ C]carfentanil BPND in healthy controls ...... 164 4.4. Repeated measures ANOVA examining the within-subject effects of Scan and ...... 165

11 ROI on [ C]carfentanil BPND in alcohol dependent participants. 4.5. Mean pre- and post-dexamphetamine [11C]carfentanil in ...... 166 alcohol dependent participants. 4.6. Mixed model ANOVA examining the within-subject effects of Scan and ROI ...... 167

11 and between-subject effect of Status on [ C]Carfentanil BPND in healthy controls and alcohol dependent participants. 4.7. Mixed model ANOVA examining the within-subject effect of ROI and ...... 167

11 between-subject effect of Status on [ C]Carfentanil ∆BPND in healthy controls and alcohol dependent participants.

11 4.8. Mean dexamphetamine-induced [ C]carfentanil ∆BPND compared between ...... 168 healthy controls and alcohol dependent participants.

21 4.9. Mixed model ANOVA examining the within-subject effect of ROI and ...... 170

11 between-subject effect of Status on [ C]carfentanil ∆BPND in alcohol dependent and gambling disorder participants.

11 4.10. Mean oral dexamphetamine-induced [ C]carfentanil ∆BPND compared between ...... 170 alcohol dependent and gambling disorder participants. 4.11. Mixed model ANOVA examining the within-subject effect of Time and ...... 172 between-subject effect of Status on plasma dexamphetamine concentrations in healthy controls and alcohol dependent participants. 4.12. Mean plasma dexamphetamine concentrations pre- and post-dexamphetamine ...... 173 challenge in healthy controls and alcohol dependent participants. 4.13. Mixed model ANOVA examining the within-subject effect of Time and ...... 177 between-subject effect of ‘High’/’Low’ responder on serum cortisol concentration in ‘high’- and ‘low’-cortisol responding alcohol dependent participants. 4.14. Mixed model ANOVA examining the within-subject effect of Time and ...... 179 between-subject effect of Status on serum cortisol concentration in healthy controls and alcohol dependent participants. 4.15. Mixed model ANOVA examining the within-subject effect of Time and ...... 179 between-subject effect of Status on serum cortisol concentration in healthy controls and ‘low’-cortisol releasing alcohol dependent participants. 4.16. Repeated measures ANOVAs examining within-subject effects of Time and ...... 181 SAIRS subscale, and ANOVAs examining the within-subject effects of Time in each individual SAIRS subscale in healthy controls. 4.17. Repeated measures ANOVAs examining within-subject effects of Time and ...... 183 SAIRS subscale, and ANOVAs examining the within-subject effects of Time in each individual SAIRS subscale in alcohol dependent participants. 4.18. Mixed model ANOVA examining within-subject effects of Time and ...... 186 SAIRS subscales and between subject effect of Status on SIARS scores in healthy controls and alcohol dependent participants. 4.19. Mixed model ANOVA examining the within-subject effect of ROI and ...... 191

11 between-subject effect of Smoking on [ C]carfentanil ∆BPND in healthy controls 4.20. Mixed model ANOVA examining the within-subject effect of ROI and ...... 191

11 between-subject effect of Smoking on [ C]carfentanil ∆BPND in alcohol dependent participants. 4.21. Mixed Model ANOVA examining the within-subject effect of ROI and ...... 192

11 between-subject effects of Smoking and Status on [ C]carfentanil ∆BPND in healthy controls and alcohol dependent participants.

22 4.22. Mixed model ANOVA examining within-subject effects of ROI and ...... 193

11 between subject effect of Genotype on [ C]carfentanil ∆BPND in healthy controls and alcohol dependent participants.

5.1. Demographic measures for fMRI dataset ...... 221 5.2. MID task behavioural measures in fMRI dataset ...... 222 5.3. Mixed model ANOVA examining the within-subject effect of BOLD fROI and ...... 223 between-subject effect of Status on MID win>neutral anticipation %BOLD change in healthy controls, alcohol dependent and gambling disorder participants. 5.4. Demographic measures in combined PET and fMRI dataset ...... 225 5.5. MID task behavioural measures in combined PET and fMRI dataset ...... 226

11 5.6. Correlations between [ C]carfentanil BPND and MID win>neutral anticipation ...... 227 %BOLD signal change. 5.7. Summary results of FSL FLAME models examining the correlations between ...... 232

11 MID win>neutral anticipation BOLD contrast and baseline [ C]carfentanil BPND.

11 5.8. Correlations between [ C]carfentanil ∆BPND and MID win>neutral anticipation ...... 233 %BOLD signal change. 5.9. Summary results of FSL FLAME models examining the correlations between ...... 238

11 MID win>neutral anticipation BOLD contrast and [ C]carfentanil ∆BPND. 5.10. Mixed model ANOVA examining the within-subject effect of BOLD ROI and the ...... 239 between-subject effects of Status and OPRM1 G-allele in healthy controls, alcohol dependent and gambling disorder participants.

23 ACKNOWLEDGEMENTS

The funding for the work in my PhD thesis was provided by a MARC clinical fellowship funded by Imperial BRC. The clinical research in this thesis was funded by the MRC (Grant G1002226 awarded to Professors David Nutt and Anne Lingford-Hughes).

I would like to thank everyone who has provided me with support, inspiration and guidance during the past four years, particularly:

My supervisor Professor Anne Lingford-Hughes for her mentorship and extensive support over the past four years as well her constant encouragement to expand the boundaries of my knowledge and abilities.

My co-supervisor Professor David Nutt for the initial opportunity to work at the Neuropsychopharamcology Unit eight years ago, his ongoing support and bottomless font of knowledge regarding everything psychopharmacological (and everything else).

Dr Jim Myers, although not officially one of my supervisors, for providing me with huge amounts of his time, advice, and support during my PhD, particularly with the technical aspects of the PET data analysis.

Dr Liese Mick, Dr Alessandro Colasanti and Dr Abhishekh Ashok for their hard work scanning healthy controls and gambling disorder participants, their advice and supporting me to use this data in my thesis.

Dr John McGonigle and Dr Louise Paterson for their guidance with the ICCAM MRI data processing, as well as understanding and analysing the ICCAM MID task. Dr Jimmy (Chen Chia) Lan for his support with FSL and MATLAB and Dr Liam Nestor for his advice regarding the FSL analyses. Claire Wilkinson who assisted with collating the large healthy control, alcohol dependent and gambling disorder participant dataset for the combined PET fMRI analysis. Dr Claire Durant for the long days and early morning EEG.

I have been privileged to spend over four years in the Neuropsychopharmacology Unit and would like to additionally thank Ash, Robin T, David E, Andre, Vicky and all the current and past members who have participated in our lunchtime rituals, ‘coffee room’ debates and increasing the noise level in the 5th floor bays and, most importantly, their friendship .

It has been a pleasure to work with all the staff at Imanova/Invicro including Dr Illan Rabiner, Professor Roger Gunn, Ryan Janisch, Mark Tanner, James Davies, Jim Anscombe, Daniela Ribiero and Rohini Akosa. Thanks to you every study visit ran as smoothly as could be hoped and there was never a moment!

The NIHR Imperial clinical research facility for providing facilities to screening participants and the CRF clinical staff for their support.

Staff and volunteers in clinical services including CNWL, WDP and Addaction who supported the recruitment of research participants.

Dr Alex Moore and the staff in Brent drug and alcohol services for hosting me for the past 3 years. It has been such a unique opportunity to learn from all of you.

My family (Mom, Dad, Jake and Rosie) and friends for providing endless enjoyable diversions and distractions, dinners, lunches, breakfasts, words of advice and sleeping space on floors/beds/sofas.

And finally, Fanny, thank you for your patience, wisdom and inspiration (and perfect guidance for good work ethic). I wouldn’t have managed these last 4 years without you, and I’m looking forward to all our shared adventures ahead!

This thesis is dedicated to all the participants who generously gave their time for this research.

26 LIST OF ABBREVIATIONS

∆BPND Proportional change in BPND (Binding Potential Non-Displaceable) 5HT 5-hydroxytryptamine (Serotonin) A Adenine AD Alcohol Dependent ADS Alcohol Dependence Scale AFNI Analysis of Functional NeuroImages ALE Activation Likelihood Estimate AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid ANOVA Analysis of variance ANTs Advanced Normalization Tools AUC Area Under the Curve AUQ Alcohol Urge Questionnaire BET FMRIB FSL Brain Extraction Tool BIS Barrett Impulsivity Scale Questionnaire-11 BDI Beck Depression Inventory II BDNF Brain-derived neurotrophic factor BMI Body Mass Index BP Binding Potential

BPND Binding Potential Non-Displaceable BOLD Blood Oxygen Level Dependent CBT Cognitive Behavioural Therapy CDT Cluster Determining Threshold CIC Clinical Imaging Centre atlas CIWA Clinical Institute Withdrawal Assessment CNS Central Nervous System COPE Contrast of Parameter Estimates CT Computerised Tomography

D2 Dopamine receptor D2

D3 Dopamine receptor D3

27 DAMGO [D-Ala2, N-MePhe4, Gly-ol]-enkephalin DAT Dopamine Active Transporter df Degrees of Freedom DLPFc Dorsolateral Pre-Frontal Cortex DNA Deoxyribonucleic Acid DSM-5 Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ECG Electrocardiogram EDTA Ethylenediaminetetraacetic Acid EPI Echoplanar Image EV Explanatory Variable FDG 2-deoxy-2-[18F]fluoro-D-glucose FH-/FH+ Family History Negative And Positive FLAME FMRIB's Local Analysis Of Mixed Effects fMRI Functional Magnetic Resonance Imaging FMRIB Functional Magnetic Resonance Imaging of the Brain Analysis Group fROI ‘Functional’ Region Of Interest FSL FMRIB Software Library FTND Fagerström Test for Nicotine Dependence FWE Family Wise Error G Guanine GABA gamma-aminobutyric acid GD Gambling Disorder GLM General Linear Model GP General Practitioner HC Healthy Control HRF Haemodynamic Response Function ICCAM Imperial College, Cambridge, Manchester LFT Liver Function Test MBq Megabecquerel MPFc Medial Prefrontal Cortex MID Monetary Incentive Delay MNI Montreal Neurological Institute

28 MOR Mu-Opioid Receptor MPRAGE Magnetisation-Prepared Rapid Acquisition Gradient-Echo Sequence MPFc Medial Pre-Frontal Cortex MRI Magnetic Resonance Imaging NAcc Nucleus Accumbens NET Norepinephrine Transporter NIfTI Neuroimaging Informatics Technology Initiative NMDA N-Methyl-D-Aspartate NOAA Neurotransmitters in Opioid and Alcohol Addiction OFc Orbitofrontal cortex OPRM1 Opioid Receptor Mu 1 PET Positron Emission Tomography PHNO Propyl-Hexahydro-Naphtho-Oxazin POMC Proopiomelanocortin Precentral G. Precentral Gyrus ROI Region Of Interest SADQ Severity of Alcohol Dependence Questionnaire SAIRS Simplified Amphetamine Interview Rating Scale SERT Serotonin Reuptake Transporter SMA Supplementary Motor Area SNP Single Nucleotide Polymorphism SPM12 Statistical Parametric Mapping version 12 SRTM Simplified Reference Tissue Model SUV Standardised Uptake Values SSAI Spielberger State Anxiety Scale STAI Spielberger Trait Anxiety Scale UPPS-P Urgency, Premeditation (lack of), Perseverance (lack of), Sensation Seeking, Positive Urgency (UPPS-P) Impulsive Behaviour Scale TAC Time Activity Curve THC Tetrahydrocannabinol TRQ Time to Relapse Questionnaire VD Volume of Distribution

29 V. Pal Ventral Pallidum VTA Ventral Tegmental Area WHO World Health Organisation

30 CHAPTER 1: INTRODUCTION

1.1. Alcohol dependence

Alcohol dependence is an addictive disorder characterised by the loss of control over alcohol consumption. It is also associated with physical alcohol withdrawal symptoms (including shaking, sweating, confusion, hallucinations and seizures), strong cravings to consume alcohol and continued consumption of alcohol despite negative financial, social, and health-related consequences, as well as impacting family and relatives (American Psychiatric Association, 2013). In Europe 5.5% of individuals are alcohol dependent, and the prevalence is higher in males than females (9.1% and 2.0% respectively) (Rehm et al, 2009). There is also a substantial health burden associated with alcohol misuse with 6.5% of all deaths in Europe (11.0% males, 1.8% females) attributable to alcohol (Rehm et al, 2009).

Treatment for alcohol misuse and dependence is a substantial cost to healthcare services with an estimated €5 billion spent annually in the European Union (Anderson and Baumberg, 2006). However, up to three quarters of alcohol dependent individuals will relapse to alcohol use within the first year following treatment (Miller et al, 2001) indicating a great unmet need in the effective management of alcohol dependence and its associated harms.

This thesis aims to investigate the endogenous opioid system in abstinent alcohol dependent participants, a target for opioid receptor antagonist treatment (e.g. naltrexone and nalmefene) (Rösner et al, 2010). Endogenous opioid signalling plays a key role in reward processing (Le Merrer et al, 2009) and may be a key mediator in the rewarding effects of alcohol consumption (Drobes et al, 2004; Mitchell et al, 2012) and relapse to alcohol use in abstinent alcohol dependent individuals (Hermann et al, 2017). By exploring opioid receptor availability and endogenous opioid tone using positron emission tomography (PET), and the relationship between endogenous opioid signalling and reward processing in alcohol dependence it may be possible to better understand the underlying mechanisms of effective opioid antagonist treatment.

31 1.1.1. The pharmacology of alcohol

Alcohol (ethanol) is a positive allosteric modulator of the gamma-aminobutyric acid-A (GABA-A) receptor and alcohol consumption results in higher GABA-ergic signalling. This increased GABA-A function mediates a number of the effects of alcohol intoxication including motor impairment, memory loss, anxiolysis and some of the pleasurable reinforcing effects of alcohol (Lobo and Harris, 2008; Nutt et al, 2007; Stephens et al, 2005; Werner et al, 2006). Alcohol consumption also results in lower glutamatergic signalling by reducing N-methyl-D- aspartate (NMDA) receptor and possibly also AMPA/kainite receptor function (Gonzales and Jaworski, 1997). There are compensatory changes in GABA and glutamatergic signalling associated with chronic heavy alcohol use which are primary contributors to the physical alcohol withdrawal symptoms in alcohol dependence (Addolorato et al, 2005; Leggio et al, 2008).

Acute alcohol use also affects other neurotransmission systems, including inducing a release of dopamine and endogenous opioids (Boileau et al, 2003; Mitchell et al, 2012; Ramchandani et al, 2011; Urban et al, 2010). There is also some evidence of adaptation or dysregulation in opioidergic and dopaminergic neurotransmission systems in alcohol dependence which may be contributory factors to relapse following treatment (Heinz et al, 2005; Martinez et al, 2005; Williams et al, 2009).

1.1.2. Treatment of alcohol dependence

Benzodiazepines (GABA-A receptor positive allosteric modulators) are used clinically for alcohol ‘detoxification’ to prevent dangerous withdrawal symptoms (Lingford-Hughes et al, 2012). However, benzodiazepines are primarily used to manage withdrawal symptoms and are not used long term to manage relapse risk.

Psychosocial interventions, for example cognitive behavioural therapy (CBT), are the mainstay of relapse prevention management (NICE, 2014). However, there are also a number of pharmacotherapies available to help with relapse prevention and these have a range of

32 pharmacological targets. Acamprosate acts as a functional glutamatergic NMDA antagonist and reduces the risk of relapse (Lingford-Hughes et al, 2012). Disulfiram is an aldehyde dehydrogenase enzyme blocker which leads to the accumulation of acetaldehyde leading to an adverse reaction when consuming alcohol, and disulfuram may also increase brain dopamine concentrations (Lingford-Hughes et al, 2012). Other treatments including baclofen, a GABA-B receptor agonist used to treat muscle spasticity, and topiramate, an anticonvulsant with a range of pharmacological actions, are also being investigated as pharmacotherapy for alcohol dependence (Jonas et al, 2014; Pierce et al, 2018).

1.1.3. Opioid receptor antagonists in alcohol dependence treatment

Opioid receptor antagonists (i.e. naltrexone and nalmefene) are also used as pharmacotherapy in alcohol dependence (Lingford-Hughes et al, 2012; NICE, 2014). Alcohol consumption induces a release of endogenous opioids which is associated with positive subjective effects (Mitchell et al, 2012) and opioid receptor antagonists are hypothesised to block the effects of these endogenous opioids and reduce the rewarding effects of continued alcohol consumption (Drobes et al, 2004). Naltrexone has also been shown to reduce alcohol craving during abstinence (Chick et al, 2000; O’Malley et al, 2002) and lower brain responses to alcohol cues (using functional magnetic resonance imaging) (Myrick et al, 2008), both of which may aid the recovery of individuals with alcohol dependence.

These findings, however, are not consistent across all studies, and there is heterogeneity in the responses of alcohol dependent individuals to opioid receptor antagonist treatment (Garbutt et al, 2014). There is evidence that a number of factors may mediate the efficacy of opioid receptor antagonists including a single nucleotide polymorphism of the mu opioid receptor gene (OPRM1 A118G – see Section 1.6.) and differences in opioid receptor availability (measured with positron emission tomography) (Hermann et al, 2017; Schacht et al, 2017). Higher craving in early abstinence has been associated with higher opioid receptor availability (Heinz et al, 2005; Williams et al, 2009) and this may be a potential target for opioid receptor antagonist treatment to reduce the risk of relapse.

33 A better understanding of the role of the endogenous opioid neurotransmission system in alcohol dependence, and how opioidergic signalling is dysregulated in alcohol dependence, may help to target alcohol dependent individuals who are most likely to benefit from opioid receptor antagonist treatment.

1.2. Gambling disorder

Gambling disorder is a behavioural addiction characterised by persistent and recurrent maladaptive gambling behaviour resulting in impaired functioning (American Psychiatric Association, 2013). The prevalence of gambling disorder is approximately 2% in adults and it is more common in males (Hodgins et al, 2011). Whilst gambling disorder does not involve an addictive substance, it shares a number of similarities to substance dependence including loss of control over gambling despite the negative consequences, deficient inhibitory responses and dysregulated reward sensitivity (Grant et al, 2016; van Holst et al, 2010; Verdejo-Garcıá et al, 2008). Therefore, it is a useful model of addiction to study without the confounding effects of heavy and chronic use of psychoactive substances (i.e. alcohol) on brain function and pharmacology.

Psychological treatments, such as CBT, are the mainstay management for gambling disorder (Cowlishaw et al, 2012). However, similarly to alcohol dependence, there is evidence that opioid receptor antagonists (naltrexone and nalmefene) may be beneficial in treating gambling disorder (Grant et al, 2014). There is also evidence of dysregulation of the endogenous opioid neurotransmission system in gambling disorder (Majuri et al, 2017; Mick et al, 2016), as well as changes in dopaminergic function and GABA-A receptor availability (Boileau et al, 2014; Mick et al, 2017). This suggests that changes in a range of neurotransmission systems are present in both substance and behavioural addictions.

By comparing dysregulated neurotransmission in gambling disorder with those in alcohol dependence it may be possible to elucidate which dysregulations are due chronic exposure to alcohol and which are due to the underlying process of addiction.

34 1.3. Endogenous opioid signalling in alcohol dependence and other addictions

There are three primary opioid receptors in the human brain: mu, delta and kappa (Hiller and Fan, 1996). These G-protein coupled receptors are found in both the peripheral and central nervous systems and play an important role in nociception and analgesia, reward, stress, respiration, gastrointestinal transit, endocrine and immune functions (Leknes et al, 2008; Le Merrer et al, 2009; White and Irvine, 1999). The endogenous ligands of the opioid receptors consist of β-endorphin (mu), enkephalins (delta) and dynorphins (kappa) and these peptides are produced by the cleavage of the large protein precursors proopiomelanocortin (POMC), preproenkephalin and preprodynorphin respectively (Le Merrer et al, 2009). This thesis primarily focusses on the mu-opioid receptor (MOR) and its role in reward and addiction.

1.3.1. The mu-opioid receptor (MOR)

MORs are found in high concentrations in human brain regions such as the amygdala, hypothalamus, thalamus, striatum and frontal cortex (Frost et al, 1985; Hiller and Fan, 1996; Hirvonen et al, 2009). Opioid analgesic drugs, for example morphine and fentanyl, are selective MOR agonists (Maguire et al, 1992; Pasternak and Pan, 2013) and their positive analgesic effects and negative adverse effects such as respiratory depression are primarily mediated through MOR signalling (Contet et al, 2004; Dahan et al, 2010).

MORs also play an important role in reward with MOR agonists increasing both ‘liking’ and ‘wanting’ responses to rewards (Berridge and Kringelbach, 2015; Le Merrer et al, 2009). The infusion of MOR agonists into ventral pallidum and nucleus accumbens (NAcc) reward ‘hot spots’ leads to higher hedonic responses to sucrose tastes in rats, and higher motivation to obtain the sucrose (Castro and Berridge, 2014; Peciña and Berridge, 2005; Smith et al, 2009). One potential mechanism for these effects of MOR agonists is via the modulation of mesocorticolimbic dopaminergic activity.

35 1.3.2. MORs and mesocorticolimbic dopamine signalling

The mesocorticolimbic pathway consists of dopaminergic neurons and plays an important role in reward and motivation and includes a number of brain regions important in processing reward including the ventral striatum (NAcc) (Berridge, 2012; Spanagel et al, 1992). There are GABA interneurons in the ventral tegmental area (VTA) that inhibit mesocorticolimbic dopaminergic activity, however agonism of MORs on these interneurons reduces this GABA- ergic ‘brake‘, leading to higher dopaminergic activity (Figure 1.1.) (Spanagel et al, 1992).

Mu Opioid Receptor

GABA-ergic - MOR agonism lowe rs ‘ bre ak ’ G A BA-e rg ic ac 4v ity

- GABA Inte r ne uron

Me socor 4colim bic Dopam ine ne uron Figure 1.1. – Representation of the modulation of mesocorticolimbic dopaminergic activity by MOR in the VTA. The GABA-ergic ‘break’ on dopaminergic activity (due to GABA-ergic inhibitory interneurons) is lowered by MOR agonism.

1.3.3. Substances of abuse, reward and MORs

There is evidence that MORs play an important role in the rewarding effects of substances of abuse. Opioid drugs such as heroin (diamorphine) bind directly to MORs, whilst other drugs such as dexamphetamine and alcohol induce a release of endogenous opioids that bind to MORs and this is associated with ‘high’ or ‘euphoric’ subjective effects (Colasanti et al, 2012; Mick et al, 2014; Mitchell et al, 2012).

36 MOR signalling also plays an important part in reinforcing alcohol consumption. MOR knockout mice do not self-administer alcohol (Roberts et al, 2000), and opioid receptor blockade with naltrexone reduces alcohol consumption in humans (Anton et al, 2004; O’Malley et al, 2002). This is likely through the blunting of positive subjective effects with opioid receptor antagonists, and naltrexone has been shown to blunt the positive effects of both alcohol and dexamphetamine (Davidson et al, 1999; Jayaram-Lindström et al, 2004, 2008; McCaul et al, 2000; Volpicelli et al, 1995). However, this blunting of positive subjective effects may be due to a mechanism other than modulating striatal dopaminergic signalling. For example, naltrexone blunts dexamphetamine-induced euphoria but does not lower dexamphetamine-induced striatal dopamine release (Jayaram-Lindström et al, 2017).

1.3.4. MORs, endogenous opioid signalling and addiction

In addition to the direct association between endogenous opioid signalling and the rewarding effects of drugs and alcohol, there is also evidence that a dysregulation of endogenous opioid signalling (MORs and endogenous opioids) may play an important role in addiction (Contet et al, 2004).

Addiction has been described as a ‘reward deficient’ state where drugs or alcohol are used to compensate for this deficiency (Oswald and Wand, 2004; Ulm et al, 1995). It is theorised that this reward deficiency is associated with an ‘opioid deficiency’ (Oswald and Wand, 2004; Ulm et al, 1995), and individuals with a family history of alcohol dependence have lower peripheral β-endorphin concentrations (Dai et al, 2005) which may reflect an ‘opioid deficiency’ risk factor for developing addiction.

There is also evidence that endogenous opioid signalling is dysregulated in addiction with higher MOR availability during early abstinence in both alcohol and cocaine dependence (Ghitza et al, 2010; Gorelick et al, 2005, 2008; Heinz et al, 2005; Weerts et al, 2011). This high MOR availability is associated with higher craving in alcohol and cocaine dependence and a higher risk of relapse in cocaine dependence (Ghitza et al, 2010; Gorelick et al, 2008; Heinz et al, 2005) and so may be an important factor in recovery. It is theorised that this high MOR

37 availability is a target for opioid receptor antagonist treatment in alcohol dependence (Hermann et al, 2017).

Endogenous opioid tone, measured with [11C]carfentanil positron emission tomography (PET) and oral dexamphetamine challenge (see Section 1.5.5.), also appears to be lower in gambling disorder (Mick et al, 2016) which may be consistent with the theory of an opioid deficiency in addiction.

1.4. Positron Emission Tomography (PET) imaging

The studies discussed above examining MORs and endogenous opioid release in addiction used Positron Emission Tomography (PET) imaging. PET uses radiolabelled compounds (‘radiotracers’) to image molecular targets, such as opioid receptors, in the brain in vivo (Innis et al, 2007). PET imaging can be used to investigate differences in neuroreceptor availability in a range of conditions such as obesity or addiction (Ashok et al, 2017; Heinz et al, 2005; Tuominen et al, 2015).

1.4.1. Overview of PET imaging

The radiotracers used in PET contain a short half-life (t½) positron emitting radionuclide (e.g. carbon-11, t½ ~20 minutes). When the radionuclide decays it releases a positron which then annihilates with an electron leading to the release of two gamma ray photons travelling in opposite directions (at 180o). The PET camera, which consists of a ring of detectors, measures the coincident arrival of this pair of photons at opposite points of the PET camera ring. The source of the emission can be localised along this 180o line of coincident detection. Once the PET scan is completed, the data consisting of all the coincident detections were processed to construct a 3-dimentional image. Processing of the image includes attenuation correction, which may use attenuation data from a rotating Germanium-68 source, or an x-ray computerised tomography (CT) scan. Detector events due to scattered photons or random

38 coincidences also have to be accounted for as part of reconstruction (Rizzo et al, 2017; Walker et al, 2004). Following the collection and reconstruction of the PET data, modelling is required to estimate the signal of interest in the brain.

1.4.2. Overview of PET kinetic modelling

Kinetic modelling of PET data allows the distribution of the radiotracer in the brain to be measured over time following the intravenous injection of the radiotracer. The scanner data may be split into ‘frames’ which are time periods during which all processed coincident detection data are combined. Typically, these frames will be shorter at the start of the scan and become longer as the scan progresses to reflect the decay of the radioisotope requiring longer time periods for similar numbers of coincident detections. These data also undergo decay correction to account for the decay of the radioisotope during the scan (Rizzo et al, 2017).

Time-activity curves (TACs) are then generated for brain volumes of interest, which could range from the whole brain to a brain region or a single voxel (typical resolution of 2x2x2 mm brain volume). TACs represent the mean activity within a volume over time, and when TACs are generated from decay corrected data any changes in activity represent a reduction in the concentration of the radioisotope in the volume, which typically occurs due to metabolism or excretion of the radioligand (Rizzo et al, 2017).

Kinetic modelling of dynamic PET data allows quantitative measurements of binding sites to be made. Kinetic modelling is required to separate the ‘specific’ signal of interest (e.g. radioligand bound to the MOR in a volume of interest) from ‘non-specific’ (e.g. radioligand bound to anything that is not MORs such as other proteins) and ‘free’ (e.g. unbound radioligand) in the same tissue (Innis et al, 2007). These different states of the radioligand are referred to as ‘compartments’, and kinetic modelling attempts to account for which portion of the signal is from each compartment to allow an accurate estimation of specific binding in a tissue volume (Morris et al, 2004; Rizzo et al, 2017).

39 1.4.3. Compartmental modelling in PET

Compartmental modelling requires an input function which is usually the concentration of radioligand in the blood (Ca) within area of tissue (Ct). The rate of influx of the radioligand from the blood into the tissue (K1) must also be estimated, as must the rate at which the radioligand returns to the blood (k2) (Morris et al, 2004). The net tracer flux from blood (Ca) to tissue (Ct) equals the flux into the tissue (K1Ca) minus the flux leaving the tissue (k2Ct):

!"# = & " − * " !$ ' ( + #

Figure 1.2. – Example of a one-tissue compartment PET model.

K1 Blood Tissue (Ca) (Ct) k2

As can be seen in the one-tissue compartment model shown in Figure 1.2. there is flux of radioligand from blood to tissue and also from tissue to blood, a ‘reversible’ flux of radioligand. In some cases, flux may be ‘irreversible’ and this requires a modification of the kinetic model.

Furthermore, the one-tissue compartment model does not include different components for specific and non-specific binding within the tissue compartment. This requires further compartments to be added to represent non-specific binding and unbound ‘free’ radioligand within the tissue. There may be any number of tissue compartments in a kinetic model, each of which may have reversible or irreversible kinetics (see Figure 1.3. for an example of a three- tissue compartment model).

40 Figure 1.3. – Example of a three-tissue compartment model showing free and non-

K1 k3 specifically bound radioligand, in addition to Blood Free Speciﬁc ‘specifically’ bound radioligand. (Ca) Ligand Binding k2 k4

k5 k6

Non-Speciﬁc Composite PET Binding Signal

Commonly, the compartments within the kinetic model representing signal which is not the specifically bound ligand, for example free ligand or non-specifically bound ligand, can be combined into a single non-specific compartment. An example of this can be seen in Figure 1.4. where a two-tissue compartment model has one specific binding compartment and one non-specific signal compartment.

Figure 1.4. – Example of a two-tissue compartment model where the ‘Free ligand’ and ‘non-specifically bound K1 k3 ligand’ from Figure 1.3. have been Blood Speciﬁc Non-speciﬁc collapsed into a single ‘non-specific’ (Ca) Binding k2 k4 signal compartment.

Composite PET Signal

The one-, two- and three-tissue compartment models shown above represent a system at equilibrium. The input function of these models is often estimated by measuring radioactivity counted in radial artery blood samples. Other factors that may affect the input function can also be measured from arterial blood samples including the ratio of free radioligand vs. radioligand bound to plasma proteins or other blood components, and radioactive

41 metabolites of the radioligand. These factors may also be included in the kinetic model (Morris et al, 2004).

1.4.4. Reference tissue models in PET

Quantification of receptor kinetics is possible without measuring arterial input function using a brain tissue region with no specific binding of the radioligand as a ‘reference tissue’ (Hume et al, 1992; Lammertsma et al, 1996). The simplified reference tissue model (SRTM) (Lammertsma and Hume, 1996) consists of only two tissue components: reference tissue

(CREF) and target tissue (CT) (see Figure 1.5. for an example). The ratio of specifically bound to non-specific (non-displaceable) radioligand in a tissue is calculated by comparing the ratio of signal in the target tissue region (CT) and the reference tissue (CREF). This ratio is the non- displaceable binding potential (BPND) of the target tissue (Innis et al, 2007; Lammertsma and Hume, 1996).

Figure 1.5. – Example of a simplified reference tissue

K1 model (SRTM) with reference tissue CREF and target tissue Tissue (CT). k2A (Ct)

Free Ligand in Plasma REF (CFP) K1 Reference Tissue k2REF (CRef)

Tissue

42 1.4.5. Imaging neurotransmitter release with PET

As well as imaging neuroreceptors, PET can also be used to make in vivo measurements of changes in the release or concentration of neurotransmitters that bind to the neuroreceptor target (Finnema et al, 2015).

Specific PET ligand binding of a radiotracer does not represent the ‘actual’ receptor density in a volume of tissue (Bmax) as the radiolabelled ligand may be displaced from its binding site by ‘cold’ non-radiolabelled ligand, or by endogenous ligands (Morris et al, 2004). Typically, PET radioligands are injected in humans at a ‘tracer dose’ which will occupy <1% of the target binding sites so that cold ligand displacement is negligible (Hume et al, 1998). However, endogenous ligands or neurotransmitter concentrations may have an effect on radioligand binding (Finnema et al, 2015).

Using PET imaging to measure the displacement of radioligands by endogenous neurotransmitters (Laruelle, 2000; Morris and Yoder, 2007) allows changes in the concentration of endogenous neurotransmitters including dopamine, endogenous opioids and GABA to be measured in brain regions following pharmacological, behavioural or physiological challenges (Colasanti et al, 2012; Saanijoki et al, 2018; Shotbolt et al, 2012; Stokes et al, 2014). The differences in neurotransmitter responses in different patient groups such as in addiction or schizophrenia compared with healthy controls can also be measured using PET imaging (Breier et al, 1997; Martinez et al, 2005; Mick et al, 2016).

1.5. Imaging MORs with PET and [11C]carfentanil

There are a number of opioid receptor PET ligands including non-specific [11C]diprenorphine, delta opioid receptor specific [11C]methyl-naltrindole, kappa opioid receptor specific [11C]GR103545 (Naganawa et al, 2014; Smith et al, 1999; Williams et al, 2009) and MOR specific [11C]carfentanil (Frost et al, 1985; Hirvonen et al, 2009).

43 Carfentanil is a potent and selective mu-opioid receptor agonist (Leysen et al, 1977; Stahl et al, 1977) which is routinely used in veterinary medicine to immobilise large animals (Haigh et al, 1983; Lust et al, 2011; Paterson et al, 2009). Due to the high potency of carfentanil (approximately 10,000 times that of morphine) it is not used in human medicine due to the high risk of sedation and respiratory depression (George et al, 2010; Lust et al, 2011; Riches et al, 2012). However, carbon-11 radiolabelled carfentanil ([11C]carfentanil), when limited to a maximum intravenous dose of 0.03μg/kg (e.g. 1.8μg in a 60kg individual), is safe for use in PET imaging, although there are reports of mild pharmacological effects at injected doses close to 0.03μg/kg (Newberg et al, 2009).

z 0 y 13 x -5

BPND 0 1 2 3 11 Figure 1.6. – Example of vowel-wise [ C]carfentanil binding in the human brain (BPND average from parametric SRTM images in 32 healthy controls – more details of [11C]carfentanil modelling in Chapter 2, Section 2.9.).

[11C]carfentanil selectively labels MOR over kappa and delta opioid receptors (250 and 90 times more selective respectively) with high binding in regions including the striatum and thalamus and intermediate binding in regions including the frontal cortex and anterior cingulate (Figure 1.6.) (Frost et al, 1985; Hirvonen et al, 2009). [11C]carfentanil kinetics can be modelled using SRTM (Section 1.4.4.) with the occipital lobe as a reference tissue as this region has ‘negligible’ MOR concentrations (Endres et al, 2003; Hirvonen et al, 2009). Evidence for using the occipital cortex as a reference tissue includes undetectable MOR availability in the lateral occipital cortex in post-mortem human brain autoradiography using [3H]DAMGO (Hiller and Fan, 1996), a selective MOR agonist. Furthermore, as found by Rabiner

44 et al, (2011) opioid receptor antagonist administration (both naltrexone and GSK1521498)

11 leads does not lead to dose dependent reductions in [ C]carfentanil BPND in occipital cortex in humans.

1.5.1. Investigating differences in MOR availability in humans with [11C]carfentanil PET

Whilst differences in MOR concentrations associated with alcohol dependence and other conditions can be measured post-mortem (Gabilondo et al, 1995; Hermann et al, 2017; Zalsman et al, 2005), [11C]carfentanil PET allows the imaging of MOR receptors in vivo without the confounds associated with post-mortem data.

[11C]carfentanil PET has been used to investigate changes in MOR availability in a range of conditions. For example obese individuals have lower MOR availability than controls (Burghardt et al, 2015; Karlsson et al, 2015, 2016; Tuominen et al, 2015). The associations between MOR availability and other factors has also been examined. For example higher striatal MOR availability is associated with higher cold pressor pain threshold (Hagelberg et al, 2012).

1.5.2. Imaging MOR in alcohol dependence and other addictions with [11C]carfentanil PET

MOR availability has been investigated in a number of addictions with [11C]carfentanil PET, including alcohol dependence (Bencherif et al, 2004b; Heinz et al, 2005; Hermann et al, 2017; Weerts et al, 2008, 2011), cocaine dependence (Ghitza et al, 2010; Gorelick et al, 2005, 2008; Zubieta et al, 1996), heroin dependence (Greenwald et al, 2003; Zubieta et al, 2000), smoking (Kuwabara et al, 2014; Nuechterlein et al, 2016; Ray et al, 2011) and gambling disorder (Majuri et al, 2017; Mick et al, 2016) (see Table 1.1. for details).

45 Of the four previously published [11C]carfentanil studies in alcohol dependence during early abstinence (approximately 4 days – 6 weeks of abstinence), two show significantly higher MOR availability and one shows a trend to higher MOR availability in alcohol dependence (Heinz et al, 2005; Hermann et al, 2017; Weerts et al, 2011). This is in keeping with higher opioid receptor availability shown using non-selective opioid receptor ligand [11C]diprenorphine (Williams et al, 2009). One study found significantly lower MOR availability in alcohol dependence (Bencherif et al, 2004b), however unlike the other studies in alcohol

11 dependence where kinetic modelling was used to quantify [ C]carfentanil BPND, the publication by Bencherif et al. (2004) calculated Binding Potential (BP) by averaged radioactive counts (averaged across 34 to 82 minute frames) in the target region in relation to the reference region (BP = Countregion/Countoccipital – 1). The differences in analysis methods may be a reason for the differences in the findings of Bencherif et al. study compared with the other studies.

Correlations between opioid receptor availability (MOR specific [11C]carfentanil and non- specific [11C]diprenorphine) and alcohol craving have been shown in abstinent alcohol dependent individuals. Both higher and lower MOR availability have been associated with higher craving scores (Bencherif et al, 2004b; Heinz et al, 2005; Weerts et al, 2011; Williams et al, 2009). The two studies showing high opioid receptor availability correlated with high craving scores both recruited alcohol dependent participants who required benzodiazepine prescribing to manage their alcohol detoxification (Heinz et al, 2005; Williams et al, 2009), whilst the two studies showing low opioid receptor availability associated with high craving excluded alcohol participants who required benzodiazepines during alcohol detoxication prior to, or during the PET imaging study (Bencherif et al, 2004b; Weerts et al, 2011). The lack of requirement for benzodiazepine prescribing suggests that these individuals have a lower severity of alcohol dependence with lower alcohol consumption and potentially less adaptive changes in the GABA-ergic and other neurotransmission systems (e.g. endogenous dopamine and opioids).

It is difficult to compare the severity of alcohol dependence between studies due to a variation in the methods for scoring severity. The Heinz et al. (2005) and Williams et al. (2009) studies both use the Severity of Alcohol Dependence Questionnaire (SADQ) and have

46 comparable scores (mean 35.8 and 34.5 respectively out of total score of 60). However, the Weerts et al. (2011) and Bencherif et al. (2004) studies with potentially lower severity participants did not use the SADQ. Bencherif et al. used the Alcohol Dependence Scale (ADS) and Weerts et al. only measured severity of withdrawal symptoms (Clinical Institute Withdrawal Assessment – CIWA) and craving. It is therefore difficult to quantify any differences in the severity of dependence between these studies.

The two studies comparing MOR availability between cocaine dependent individuals and controls showed higher MOR availability in cocaine dependence (Gorelick et al, 2005; Zubieta et al, 1996). High MOR availability in cocaine dependence was associated with higher craving, earlier relapse to cocaine use and increased cocaine use before treatment and during a relapse (Ghitza et al, 2010; Gorelick et al, 2005, 2008; Zubieta et al, 1996). In heroin dependence, following buprenorphine detoxification, there is also higher MOR availability compared with controls (Zubieta et al, 2000). In gambling disorder there is no evidence of higher MOR availability compared with controls, with one study showing no differences and another showing lower anterior cingulate MOR availability in gambling disorder (Majuri et al, 2017; Mick et al, 2016).

Of the three studies investigating MOR availability in smokers two have shown no differences compared with controls (Kuwabara et al, 2014; Ray et al, 2011) and one has shown lower MOR availability in smokers (Nuechterlein et al, 2016). However, these studies required smokers to be abstinent from nicotine overnight prior to scans which may affect MOR availability, and all scans were conducted following a smoking task (either de-nicotinised or ‘normal’ cigarettes) which may also affect MOR availability.

47 Table 1.1. Summary of [11C]carfentanil PET studies in addiction. Addiction Study and Additional details Results Associations with radioligand clinical variables Alcohol (Bencherif et al, DSM-IV alcohol Lower MOR Negative correlations 2004b) dependence. Inpatient, 4- availability in between MOR days abstinence before alcohol dependence availability and [11C]carfentanil PET, but no withdrawal in right dorsolateral craving in right symptoms requiring prefrontal cortex, dorsolateral treatment (low severity). right anterior prefrontal cortex, Odd PET modelling frontal cortex and right anterior frontal methods. right parietal cortex and right cortex. parietal cortex. (Heinz et al, 2005) DSM-IV alcohol Higher MOR in Higher craving dependence. Inpatient alcohol dependence correlated with MOR [11C]carfentanil detox requiring in ventral striatum in frontal lobe and benzodiazepines. 1-3 which did not ventral striatum. weeks of abstinence for change 5 weeks PET1, 4-6 weeks for PET 2. following detox. (Wand et al, 2012; DSM-IV alcohol Higher MOR in Lower MOR in Weerts et al, 2008, dependence. No alcohol dependence amygdala, ventral 2011) withdrawal symptoms across many striatum and requiring treatment – low regions. Trend to thalamus associated [11C]carfentanil and severity. PET on day 5 of higher DOR, but with higher craving in [11C]methyl- abstinence and then day non-significant. alcohol dependence. naltrindole 15 after 10 days naltrexone treatment. (Hermann et al, DSM-IV alcohol Non-significant 2017) dependence, trend to higher benzodiazepine detox MOR availability in [11C]carfentanil allowed. 3 weeks alcohol abstinence. dependence. Cocaine (Zubieta et al, DSM-IIIR cocaine Higher MOR Higher MOR 1996) dependence. Two scans, availability in availability associated one 1-4 days following last cocaine dependent with higher cocaine [11C]carfentanil cocaine use and another in early abstinence. craving during early after 4 weeks abstinence Reduction in MOR abstinence. availability with 4 weeks abstinence. (Gorelick et al, DSM-IV cocaine abuse or Higher MOR §Higher MOR 2005, 2008) dependence. 3-month availability in availability associated inpatient stay. PET Scans cocaine with higher recent [11C]carfentanil at 1 day, 1 week and 12 abuse/dependence cocaine use, cocaine weeks post admission. at 1 day which craving and earlier ‘normalises’ in all relapse to cocaine regions, except use. anterior cingulate, with extended abstinence.

48 Table 1.1. Summary of [11C]carfentanil PET studies in addiction (continued) (Ghitza et al, 2010) DSM-IV cocaine abuse or No comparisons Higher MOR dependence receiving with controls associated earlier [11C]carfentanil outpatient treatment. relapse to cocaine use and higher cocaine use during treatment. Heroin (Zubieta et al, Non-treatment seeking Higher MOR 2000) heroin dependent (no availability in heroin diagnostic criteria listed). dependence [11C]carfentanil 1st Scan during 6-week compared with buprenorphine controls following maintenance, 2nd scan buprenorphine following 8-day detox detox. from buprenorphine. Gambling (Mick et al, 2016) DSM-IV pathological No differences in In gamblers high gambling in outpatient MOR availability caudate MOR [11C]carfentanil treatment. between gamblers correlated with and controls. higher impulsivity. (Majuri et al, 2017) DSM-IV pathological Lower anterior gambling cingulate MOR [11C]carfentanil availability in gambling disorder Smoking (Ray et al, 2011) Current smokers. 2 scans, No differences 1 following smoking between controls [11C]carfentanil nicotine containing and smokers. cigarette and 1 following de-nicotinised cigarette. 14-hour abstinence from nicotine. (Kuwabara et al, Current smokers. 2 scans, No differences 2014) 1 following smoking between controls nicotine containing and smokers. [11C]carfentanil cigarette and 1 following placebo cigarette. Overnight abstinence from nicotine. (Nuechterlein et al, Current smokers. 2 scans: Lower MOR 2016) during 1 smoked nicotine availability in containing cigarette and smokers compared [11C]carfentanil during the other a de- with controls. nicotinised cigarette. 8 to 12-hour abstinence from nicotine.

49 1.5.3. Measuring endogenous opioid release with [11C]carfentanil PET

In addition to selectively radiolabelling MOR in vivo, [11C]carfentanil binding to MORs is also sensitive to endogenous opioids concentrations. This means [11C]carfentanil PET can be used to measure changes in endogenous opioid concentrations in the brain as higher endogenous opioid concentrations will lead to reductions in [11C]carfentanil specific binding (Colasanti et al, 2012; Nummenmaa et al, 2016).

Whilst β-endorphin is the endogenous opioid neurotransmitter with the highest affinity for the MOR, a number of other endogenous ligands also bind to MOR including enkephalins, and endomorphins (Quelch et al, 2014). Furthermore, β-endorphin precursor POMC is not present in some high MOR concentration brain regions and therefore other endogenous opioid peptides are the primary MOR ligand (Le Merrer et al, 2009). For example in the putamen enkephalins rather than β-endorphin are the primary MOR ligand (Banghart et al, 2015). Therefore, changes in [11C]carfentanil binding observed across a range of brain regions following a pharmacological or non-pharmacological challenge are likely to reflect changes in the concentration of a range of endogenous MOR ligands rather than solely changes in β- endorphin concentrations.

A number of behavioural, physiological and pharmacological challenges have been shown to alter the binding of [11C]carfentanil in a range of brain regions. The effects of pain on [11C]carfentanil binding and endogenous opioid release, particularly the release of endogenous opioids following placebo administration, has been the most thoroughly studied in the literature (Bencherif et al, 2002; DosSantos et al, 2014; Love et al, 2009; Ly et al, 2013; Martikainen et al, 2013; Peciña et al, 2015b; Scott et al, 2007b, 2008; Wager et al, 2007; Zubieta et al, 2002, 2003a; Zubieta, 2005). This is unsurprising given the key role MORs play in pain responses and analgesia. There is also evidence in depression that differences in endogenous opioid release are associated with antidepressant responses. Individuals with major depression who have higher endogenous opioid release following administration of a placebo antidepressant have a better response to antidepressant treatment (Peciña et al, 2015a).

50 There are a number of task or behavioural paradigms that have been used to investigate endogenous opioid release using [11C]carfentanil PET. Changes in endogenous opioid release associated with feeding (Burghardt et al, 2015; Tuulari et al, 2017), exercise (Saanijoki et al, 2018), sadness (Zubieta et al, 2003b), social touch and laughter (Manninen et al, 2017; Nummenmaa et al, 2015) and acupuncture (Harris et al, 2009) have also been demonstrated in humans using [11C]carfentanil PET.

The effects of pharmacological challenges on endogenous opioid release have also been investigated with [11C]carfentanil PET in both healthy controls and individuals with addiction. Most of the published pharmacological challenge studies investigated the effect of smoking

11 on [ C]carfentanil BPND in smokers, although the results are mixed with increases and

11 decreases or no differences in [ C]carfentanil BPND reported after smoking nicotinised cigarettes compared with a de-nicotinised or placebo cigarettes (Domino et al, 2015; Kuwabara et al, 2014; Nuechterlein et al, 2016; Ray et al, 2011; Scott et al, 2007a). A single

11 study showed reductions in [ C]carfentanil BPND following an oral challenge in the NAcc (ventral striatum) of both non-addicted heavy drinkers and healthy controls, and greater

11 reductions in [ C]carfentanil BPND (i.e. higher endogenous opioid release) were associated with higher reported subjective euphoria and drunkenness (Mitchell et al, 2012).

1.5.4. [11C]carfentanil PET and dexamphetamine challenge-induced endogenous opioid release

Dexamphetamine is the more potent/active enantiomer of amphetamine and increases concentrations of synaptic monoamines by inhibiting reuptake via the dopamine (DAT), norepinephrine (NET) and 5HT/serotonin (SERT) transporters, and through the release of monoamines from synaptic vesicles (Robertson et al, 2009). Human PET research has shown dexamphetamine-induced dopamine release can be measured in the striatum using a number

11 of PET ligands including selective D2 antagonist [ C]raclopride (Breier et al, 1997; Jayaram-

Lindström et al, 2008; Leyton et al, 2002; Martinez et al, 2003; Munro et al, 2006), D2/D3 agonist ligands [11C]-(+)-PHNO (Boileau et al, 2014; Shotbolt et al, 2012) and [11C]NPA

18 (Frankle et al, 2018) and D2/D3 antagonist [ F]fallypride (Narendran et al, 2009; Slifstein et

51 al, 2010; Smith et al, 2019). Furthermore, [11C]FLB457 has been shown to measure dexamphetamine-induced dopamine release in cortical regions (Narendran et al, 2009, 2011,

11 2014; Slifstein et al, 2015). There is also evidence that 5HT2A specific agonist ligand [ C]Cimbi- 36 is displaced following an oral dexamphetamine challenge (Erritzoe et al, 2017).

Dexamphetamine has been shown to induce an increase in β-endorphin concentrations with micro-dialysis in rats and peripheral blood sampling in humans (Cohen et al, 1981; Olive et al,

11 2001). There are reductions in [ C]carfentanil BPND (i.e. endogenous opioid release) three hours following an oral 0.5mg/kg dexamphetamine challenge in healthy controls in a number of brain regions including the putamen, thalamus, insula, NAcc (ventral striatum) and frontal

11 lobe (Colasanti et al, 2012; Mick et al, 2014). The significant reductions in [ C]carfentanil BPND following the oral 0.5mg/kg dexamphetamine challenge ranged from 6.2% in the ventral striatum to 10.2% in the caudate in the Colasanti et al, (2012) study, and from 3.9% in the insula to 7.2% in the putamen in the Mick et al, (2014) study.

A study examining the SRTM test-retest variability of [11C]carfentanil using a similar scanning protocol to the oral 0.5mg/kg dexamphetamine challenge studies, with scan one in the morning and scan two in the afternoon of the same day, found the test-retest variability was

11 lowest in ROIs with higher [ C]carfentanil BPND, for example the thalamus had the highest mean BPND of 2.31 and lowest test-retest variability of 3.68%, whilst the mesial temporal cortex had the lowest mean BPND of 0.52 and highest test-retest variability of 9.58% (Hirvonen

11 et al, 2009). Whilst the significant reductions in [ C]carfentanil BPND observed following the oral 0.5mg/kg dexamphetamine challenge are in a similar range to the same day test-retest variability (i.e. 3.68 to 9.58%), Colasanti et al, and Mick et al, examined ROIs with higher BPND

(i.e. all ROIs above a BPND of 1.00, and half over BPND over 1.5) where there is lower test-retest variability.

Colasanti et al. (2012) found that greater reductions in ventral striatum (NAcc) [11C]carfentanil

BPND (i.e. higher endogenous opioid release) were associated with higher reported euphoria. The positive subjective effects of dexamphetamine can also be lowered with opioid receptor antagonist naltrexone (Jayaram-Lindström et al, 2004, 2008), suggesting a key role for endogenous opioid signalling in the rewarding effects of dexamphetamine.

52 11 It is likely that the oral 0.5mg/kg dexamphetamine-induced reductions in [ C]carfentanil BPND are due to higher internalisation of MOR rather than direct competition of MOR binding between [11C]carfentanil and endogenous opioid ligands (Colasanti et al, 2012; Quelch et al, 2014). MORs are internalised after agonist binding and [11C]carfentanil does not bind to internalised MOR in the endosomal compartment (Quelch et al, 2014). It also appears that

11 this process takes some time to be measurable with reduced [ C]carfentanil BPND because a 0.3 mg/kg intravenous dexamphetamine challenge administered immediately prior to the PET

11 scan does not lead to a reduction in [ C]carfentanil BPND (Guterstam et al, 2013).

1.5.5. [11C]carfentanil and dexamphetamine challenge in gambling disorder

The oral 0.5mg/kg dexamphetamine challenge and [11C]carfentanil PET protocol has been used to measure dysregulation of endogenous opioid release in gambling disorder. Dexamphetamine-induced endogenous opioid release was found to be blunted in gambling disorder in five out of ten regions of interest (ROIs) investigated (frontal lobe, insula, anterior cingulate, putamen and cerebellum). It was also found that individuals with gambling disorder have blunted ‘euphoric’ and ‘alert’ subjective responses compared with healthy controls following oral dexamphetamine. However, no clinical measures, for example gambling severity, were significantly associated with the blunted endogenous opioid release in gambling disorder participants (Mick et al, 2016).

1.6. The MOR OPRM1 A118G polymorphism

The OPRM1 A118G single nucleotide polymorphism (SNP – rs1799971) is the most common SNP in the coding region of the MOR gene: OPRM1 (Wang et al, 2012b). This SNP is associated with a replacement of adenine (A) at position 118 in exon 1 by guanine (G), which results in the substitution of asparagine (Asn) at position 40 with aspartic acid (Asp) in the N-terminal domain leading to the removal of one of five potential N-linked glycosylation sites of the receptor (Bond et al, 1998; Wang et al, 2012b). The frequency of the G-allele varies with a

53 range from 10 –14% in Caucasian populations to 35– 49% in Asian populations (Kreek et al, 2005). There is human evidence of potential functional changes associated with the OPRM1 A118G polymorphism, for example G-allele carriers require higher opioid doses to manage pain (Hwang et al, 2014) and the G-allele may be associated with differential subjective responses to alcohol (Ray et al, 2013).

1.6.1. In vitro functional effects of OPRM1 A118G polymorphism

The A118G polymorphism alters the structure of the MOR, however the in vitro evidence of the effect of the A118G polymorphism on the function of the MOR receptor is mixed. OPRM1 G-allele allele has been associated with higher MOR β-endorphin affinity in one published study by Bond et al. (1998), but this was not replicated in two further published studies which found no differences in β-endorphin affinity (Befort et al, 2001; Beyer et al, 2004). Bond et al. also found higher β-endorphin activation at the MOR associated with the OPRM1 G-allele, although this has also not been replicated by three other published studies (Befort et al, 2001; Beyer et al, 2004; Kroslak et al, 2007). There is also some evidence of lower MOR affinity of diprenorphine (Zhang et al, 2005) and lower MOR activation by exogenous opioid drugs (morphine, methadone and DAMGO) (Kroslak et al, 2007), but again these results have not been replicated by other published studies (Befort et al, 2001; Beyer et al, 2004). These in vitro studies used a range of different transfected cell lines, including Syrian hamster kidney AV12 cells (Bond et al, 1998; Kroslak et al, 2007), Chinese hamster ovary cells (Zhang et al, 2005) and human embryonic kidney cells (Beyer et al, 2004; Kroslak et al, 2007). It is possible that differences in the type of cell lines used may affect the reproducibility of some of these results.

1.6.2. OPRM1 A118G polymorphism and alcohol dependence

There is some published evidence that the OPRM1 G-allele is associated with higher alcohol consumption and higher alcohol related subjective effects and craving indicating that the OPRM1 G-allele may be a risk allele for alcohol dependence (Hendershot et al, 2016; Ray et

54 al, 2012, 2013; Ray and Hutchison, 2004; van den Wildenberg et al, 2007). Higher alcohol consumption, craving and subjective effects of alcohol may be associated with enhanced alcohol-induced striatal dopamine release in G-allele carriers, as shown with intravenous alcohol and [11C]raclopride PET (Ramchandani et al, 2011). However, one recent publication did not find any differences in alcohol self-administration associated with the OPRM1 A118G polymorphism (Sloan et al, 2018) and there is also recent evidence that the higher alcohol- induced subjective responses in G-allele carriers may be mediated by a polymorphism of the dopamine transporter gene (DAT1 VNTR - rs28363170) (Ray et al, 2014; Weerts et al, 2017).

A number of large sample studies and meta-analyses have examined the association between the OPRM1 A118G polymorphism and the risk of developing alcohol dependence or substance dependence. Of the two studies focussing on alcohol dependence only, one large Finnish population-based sample showed no effect of the OPRM1 A118G polymorphism on alcohol dependence risk (Rouvinen-Lagerström et al, 2013) and one meta-analysis showed an increased risk of alcohol dependence in G-allele carriers, but this was only present in Asian ethnic populations and not Caucasians (Chen et al, 2012). Of the two meta-analyses examining the OPRM1 A118G polymorphism and substance dependence, one showed a protective effect of the G-allele against substance dependence in European ancestry populations (Schwantes-An et al, 2016), whilst the other showed no significant effect of the OPRM1 A118G polymorphism on risk of developing addiction (Arias et al, 2006). The OPRM1 genotype may play a role in treatment responses in alcohol dependence to naltrexone, with evidence of longer durations of abstinence in G-allele carriers treated with naltrexone compared with A-allele carriers (Anton et al, 2008, 2012; Chamorro et al, 2012; Kim et al, 2009; Oslin et al, 2003). However, these findings are not consistent across the entire published literature with three published studies showing a lack of difference in naltrexone response associated with the OPRM A118G polymorphism (Coller et al, 2011; Gelernter et al, 2007; Oslin et al, 2015). Hermann et al. (2017) found an interaction between MOR availability and the OPRM1 A118G polymorphism in predicting treatment response, with high MOR availability G-allele carriers responding best (i.e. longer durations of abstinence) from naltrexone treatment and low MOR availability A-allele homozygous individuals having the worst response. There may also be an interaction between the OPRM1 A118G and DAT1 VNTR polymorphisms in predicting naltrexone response in alcohol dependence (Anton et al, 2012).

55 1.6.3. The OPRM1 A118G polymorphism and [11C]carfentanil PET

Compared with the literature discussed above, there is more consistency regarding some effects of the A118G polymorphism on [11C]carfentanil PET in humans. A number of studies

11 have shown lower [ C]carfentanil BPND in G-allele carriers (homozygous G:G or heterozygous G:A) (Domino et al, 2015; Nuechterlein et al, 2016; Peciña et al, 2015b; Ray et al, 2011; Weerts et al, 2013), suggesting either lower MOR availability or lower [11C]carfentanil affinity for MOR is associated with the G-allele.

1.6.4. OPRM1 A118G polymorphism and functional MRI brain responses

The effects of the OPRM1 A118G polymorphism on functional brain responses has been less thoroughly studied, and the majority of the published studies relate to alcohol use. The three published functional magnetic resonance imaging (fMRI) studies examining the effects of the OPRM1 A118G polymorphism on brain responses to alcohol taste have inconsistent findings with higher and lower functional brain responses, using Blood Oxygen Level Dependent (BOLD) fMRI (see Section 1.7. for more details regarding fMRI), in G-allele carriers (Filbey et al, 2008; Korucuoglu et al, 2017) as well as no differences between G-allele carriers and A-allele homozygous individuals (Ziauddeen et al, 2016). Bach et al. (2015) showed that OPRM1 G-allele carrying abstinent alcohol dependent individuals have higher functional brain BOLD responses to alcohol cues (Bach et al, 2015), and whilst Schacht et al. (2013) did not replicate this finding, they did find an interaction between G-allele carriers and naltrexone on alcohol cue functional brain BOLD responses (Schacht et al, 2013b).

The association between the OPRM1 A118G genotype and functional brain BOLD responses to social ‘pain’ or rejection has been examined in two published papers, both of which showed greater functional brain BOLD responses to social pain in G-allele carriers (Bonenberger et al, 2015; Way et al, 2009).

56 1.7. Functional Magnetic Resonance Imaging (fMRI)

Functional Magnetic Resonance Imaging (fMRI) allows changes in brain function to be measured, for example during the anticipation of a reward using a Monetary Incentive Delay (MID) task (Knutson et al, 2000). These reward anticipation responses have been shown to be dysregulated in alcohol dependence and other addictions (see Section 1.8. for details) and this may be linked to dysregulated endogenous opioid signalling (Nestor et al, 2017).

Magnetic resonance imaging (MRI) uses a powerful magnetic field (i.e. the MRI scanner) to image tissues such as the brain. Protons (hydrogen nuclei) in tissues containing water align with the scanner’s high-power magnetic field, and the scanner then uses radio-frequency pulses to oscillate protons between ‘high’ and ‘low’ energy states. These oscillations cause the protons to emit radio-frequency signals which are then measured by the scanner. Different MRI sequences can be used to collect different types of data, for example static structural three-dimensional images of tissues (e.g. T1 weighted and T2 weighted images) (Castellaro et al, 2017).

1.7.1. Blood Oxygen Level Dependent (BOLD) fMRI

The Blood Oxygen Level Dependent (BOLD) signal is one fMRI technique used to measure real- time changes in brain activity. Higher neural activity in a brain region leads to higher local metabolic activity and increases the oxygen requirement for this area of brain tissue. This results in a transient increase in local blood flow known as the Haemodynamic Response Function (HRF). During the HRF there is a relative increase in oxygenated haemoglobin compared with deoxygenated haemoglobin due to higher blood flow supplying more oxyhaemoglobin to the area of higher metabolic requirement. This change in the oxyhaemoglobin to deoxyhaemoglobin ratio can be measured with the MRI scanner due to the differences in the magnetic properties of oxygenated compared with deoxygenated haemoglobin (Buxton, 2013; Castellaro et al, 2017).

57 BOLD responses are typically measured either at rest (e.g. resting state fMRI) (Lee et al, 2013) or during a task which may involve a range of stimuli including visual, auditory or tactile inputs and responses from participants (Castellaro et al, 2017). During task-based fMRI the BOLD signal during the stimulus of interest has to be contrasted with BOLD signal during a neutral stimulus as the BOLD signal is not a quantifiable measure of neural function. This is due to the complex underlying physiology of the BOLD signal including changes in cerebral blood flow, regional metabolic requirements and oxygenation of haemoglobin all of which may have conflicting effects on the BOLD signal (Buxton, 2013). Therefore, the outcome of task-based fMRI is a contrast of the BOLD signal during two conditions, an example discussed below is the monetary incentive delay task where the ‘win anticipation’ BOLD signal is contrasted with the ‘neutral anticipation’ BOLD signal (see Section 1.8.).

1.7.2. Pre-processing of task-based fMRI data fMRI BOLD data typically consist of a time series of volumes of brain images (e.g. Echoplanar Images – EPIs) acquired across the duration of the task. Each volume is usually 2 seconds duration and therefore a 60 second task consists of 30 x 2 second volumes. The fMRI data require pre-processing prior to modelling of the task (Castellaro et al, 2017).

Pre-processing of fMRI BOLD data usually consist of (Castellaro et al, 2017): 1. A high pass filter to correct for noise that might cause a slow baseline drift of signal, such as instabilities in scanner hardware. 2. Motion correction across the volumes of the data corrects for any movement of the participant in the bore of the scanner during the task. 3. Spatial smoothing of the data within each volume is used to limit the effect of inter- voxel noise. 4. Registration to a standard template space is required to compare BOLD signal data across multiple subjects. Typically, an individual’s structural MRI scan will be used to derive transformation parameters for the fMRI data volumes due to the higher spatial resolution of structural data.

58 1.7.3. Modelling of task-based fMRI data

After pre-processing the modelling of the task-based BOLD data (i.e. the EPIs from the task- based scan) can be carried out. The most common method for this is using the General Linear Model (GLM) (Friston et al, 1995). The GLM assumes that each voxel is independent and its BOLD signal across the volumes of the task can be decomposed into components representing separate signal sources. Explanatory variables (EVs) are used as regressors in the analysis and are modelled a priori to contain the time course of task-related stimuli (based upon the design matrix of the task) and other nuisance variables that may influence the BOLD signal, such as motion (Castellaro et al, 2017; Poline and Brett, 2012). The output from the GLM model in task-based fMRI will typically be a number of single-subject whole brain voxel-wise BOLD contrasts of interest compared between stimuli (e.g. win anticipation BOLD > neural anticipation BOLD).

Statistical analyses can then be carried out using these whole brain BOLD contrasts, for example t-tests between patients and controls, or correlations with other variables, and can include covariates to account for variables such as demographic differences between subjects or groups of subjects (Beckmann et al, 2003; Castellaro et al, 2017). Due to the large numbers of voxels within the GLM and other fMRI statistical analyses, multiple comparison correction is required to reduce the chance of type I errors (false positives). Bonferroni correction has been shown too conservative for fMRI multiple comparison correction (Logan and Rowe, 2004), and therefore Family Wise Error (FWE) correction is commonly used although it has an increased risk of type II errors (i.e. false negatives) (Eklund et al, 2016).

59 1.8. Monetary Incentive Delay (MID) Task

The Monetary Incentive Delay (MID) task is an event-related fMRI paradigm used to probe reward sensitivity. This task was developed by Knutson et al. to examine brain BOLD responses during anticipation of, and in response to, winning financial rewards (Knutson et al, 2000).

The MID task consists of a number of trials, each of which start with the presentation of a cue indicating the type of trial that is about to be performed: e.g. win, neutral or loss trial. Following the cue there is an ‘anticipation period’ during which participants are waiting for a target symbol to be presented. The target symbol is displayed for a short period of time and participants must react (i.e. pushing a button on a response box) within this period of time to ‘hit’ the target to win a money in a win trial or avoid losing money during a loss trial. Following the target period, participants are presented with feedback regarding the outcome of the trial (see Chapter 2, Figure 2.9. for an example of the MID task cues) (Knutson et al, 2000). BOLD signal in the ventral striatum is higher during the anticipation period of win trials compared with anticipation period of neutral trials (i.e. win>neutral anticipation BOLD), and larger win>neutral anticipation BOLD contrast is associated with larger financial rewards (Knutson et al, 2000, 2001). An activation likelihood estimate (ALE) meta-analysis of the MID task in 292 healthy controls found significant win>neutral anticipation BOLD contrast in bilateral putamen, right insula and left inferior prefrontal gyrus clusters (McGonigle et al, 2017).

There are a number of variations of the MID task. The version developed by Knutson et al., which is commonly used in the MID literature, has variable win and loss amounts (e.g. ±$0.20, ±$1.00, ±$5.00) and a neutral cue of ±$0.00 (Knutson et al, 2001). Another variant of the MID task involves a single win/loss amount (e.g. ±€1.00) and a neutral condition (McGonigle et al, 2017; Romanczuk-Seiferth et al, 2015). There is also evidence that the MID task win>neutral BOLD contrast is sensitive to pharmacological modulation. For example an oral 0.25 mg/kg dexamphetamine challenge lowers win>neutral anticipation BOLD contrast (Knutson et al, 2004).

60 1.8.1. The ICCAM platform MID task

ICCAM (Imperial College, Cambridge, Manchester) is a multi-centre fMRI and neuropsychological platform designed to investigate reward, impulsivity and emotional reactivity in addiction and how neuropharmacology modulates these processes (McGonigle et al, 2017; Paterson et al, 2015). Reward sensitivity was tested in the ICCAM platform using the MID task and this MID version had fixed win/loss amounts of £0.50 and a neutral cue.

The ICCAM MID task was designed to prioritise the contrast between win and neutral trials over loss trials with 36 win trials, 36 neutral trials but only 12 loss trials. The design of the task was also biased towards the anticipation period over the outcome period, as win anticipation BOLD responses have been the most commonly shown response to be dysregulated in addiction (McGonigle et al, 2017). More details regarding the ICCAM MID task are in Chapter 2, Section 2.12..

The number of MID trials was limited by participants’ time in the scanner as in addition to the MID task, evocative images and go-nogo tasks as well as structural imaging had to be completed within the 80 minutes scan. Therefore, the decision was made to use only a single monetary contrast (i.e. £0.50) to maximise the power to detect differences in win and neutral trial anticipation BOLD contrast at this amount, rather than using variable win/loss amounts with fewer trials at each amount with less power to detect win>neutral anticipation BOLD contrast for each amount. Furthermore, the primary aim of the ICCAM MID task was to examine win>neutral anticipation BOLD contrast (McGonigle et al, 2017) rather than the dose response effect of different monetary amounts, as shown by Knutson et al. (2001), therefore variable win/loss amounts were not required. The £0.50 win/loss amount for each trial was chosen to give a total win amount of £10.00 at the approximate task response accuracy of 66%, which was believed to be a suitable amount to motivate participants to engage in the task.

61 1.8.2. MID task in alcohol dependence

There are a number of published studies examining the MID win anticipation BOLD responses in alcohol dependence. During early abstinence there is evidence of blunted ventral striatal (NAcc) win anticipation BOLD responses in alcohol dependence (Beck et al, 2009; Hägele et al, 2015; Romanczuk-Seiferth et al, 2015; Wrase et al, 2007) and in one study win>neutral anticipation BOLD contrast was more blunted in individuals with higher alcohol craving (Wrase et al, 2007). However, these findings have not been replicated in all studies, for example two studies by Bjork et al. show no significant differences in win>neutral BOLD anticipation between healthy controls and alcohol dependent participants (Bjork et al, 2008, 2012). However the Bjork et al. studies had comorbid substance dependence in a high proportion of their alcohol dependent participants which may affect the results (Bjork et al, 2008, 2012). Other factors may have also affected the responses during the task, for example showing participants the money they could win before entering the scanner (e.g. priming), different sizes of financial rewards or variation in the task such as variable win/loss amounts or showing additional targets (summary in Table 1.2.).

62 Table 1.2. – Summary of published studies examining the MID task fMRI in alcohol dependence (healthy controls – HC, alcohol dependent – AD). Study Participants Task details Results (Wrase et al, 16 HC, 16 AD Variable win and Whole brain analysis – AD have 2007) Newly abstinent (range 5– loss amounts blunted win>neutral anticipation 37 days) (±0.10€, 0.60€, BOLD contrast in left ventral 3.00€). Cash shown striatum, right putamen and right before entering caudate nucleus. Negative scanner correlation between win>neutral anticipation BOLD contrast and alcohol craving in AD participants (Bjork et al, 23 HC, 23 ‘Substance Variable win No differences in win>neutral 2008) misuse individuals’ amounts ($0.50 or anticipation BOLD contrast (alcohol and/or other $5.00). Modified to ventral striatum responses in AD substance dependence). include repeated compared with HC Current inpatient target following treatment, abstinence some win trials. days range 6–26; mean Cash shown before 12.34 ± 5.3 task (Bjork et al, 23 HC, 29 AD Variable win No significant group differences 2012) (14 with other substance amounts ($0.00, in whole brain or extracted dependence history) $1.00, or $10.00). Ventral Striatum ROI win>neutral Cash shown before anticipation BOLD contrast. task (Beck et al, 19 HC, 19 AD. Minimum 7 Variable win-lost Lower ventral striatum 2009) days abstinence as part of amounts (0.10€, win>neutral anticipation BOLD a detox program (14 0.60€, 3.00€). Cash contrast in AD compared with HC subjects <14 days shown before task abstinent from alcohol) (Romanczuk- 21 HC, 15 AD, 20, GD. Win or lose €1. Reduced left ventral striatum Seiferth et al, AD abstinent mean 42 ± 35 Used biological win>neutral anticipation BOLD 2015) days before the fMRI parametric mapping contrast in AD compared with HC. experiment. (BPM) for analysis. No differences in win>neutral GD not in treatment, No cash shown anticipation between GD and HC, questionnaire diagnosis. before task. or AD and HC. No comorbid dependence to other substances. (Hägele et al, 54 HC, 26 AD Variable win-lost Reduced ventral striatum 2015) No other substance amounts (0.10€, win>neutral anticipation BOLD dependence history. AD 0.60€, 3.00€). No contrast in AD compared with HC. detoxed but abstinence cash shown before unknown. task. (Nestor et al, 35 HC, 20 or 21 AD Win or lose £0.50. No win>neutral anticipation ROI 2017) and Minimum 4 weeks No cash shown differences (Ventral striatum, (Murphy et abstinence, mean 14.08 ± before the task. ventral pallidum, substatia nigra). al, 2017) 4.23 weeks. Whole brain – single cluster right No comorbid other inferior frontal gyrus (IFG)/insula substance dependence with lower win>neutral anticipation BOLD contrast in AD.

63 The ICCAM platform has published two studies examining win>neutral anticipation BOLD responses in alcohol dependent participants. The ICCAM participants typically have longer durations of abstinence from alcohol compared to other studies (see Table 1.2. for details). There was no evidence of significantly different ventral striatum win>neutral anticipation BOLD contrast in the ICCAM alcohol dependent participants, although poly-substance dependent participants did show blunted ventral striatal win anticipation BOLD responses (Murphy et al, 2017). A whole brain analysis showed significantly blunted win>neutral anticipation BOLD contrast in alcohol participants in the right inferior frontal gyrus/insula and left and right caudate (Nestor et al, 2017).

A meta-analysis of MID win>neutral anticipation BOLD responses in 609 healthy controls and 643 individuals with addiction (including alcohol dependence, gambling disorder and substance dependence) showed lower ‘striatal’ (primarily dorsal striatum) win anticipation BOLD responses in addicted individuals (Luijten et al, 2017).

1.8.3. MID task in gambling disorder

There are fewer published studies examining changes in MID win BOLD responses in gambling disorder. Two of the three published studies showed lower win>neutral anticipation responses in gambling disorder compared with controls and one showed no differences (Balodis et al, 2012; Choi et al, 2012; Tsurumi et al, 2014). A modified incentive delay task including both financial and erotic image rewards showed a higher BOLD response to financial rewards compared with erotic rewards in gambling disorder participants compared with controls (Sescousse et al, 2013). This variation in win anticipation responses in gambling disorder may be due to differences in the study population. For example, the Sescousse et al. (2013) study’s participants were active gamblers who were not receiving treatment, whilst other studies such as Tsurumi et al. (2014) and Choi et al. (2012) recruited gambling disorder participants in active treatment. Some studies, such as Balodis et al. (2012), had higher levels of previous alcohol abuse or dependence comorbidity. The salience (i.e. motivation or associated ‘wanting’) of the reward in the task in relation to gambling may also be an important factor, with higher reward anticipation shown in gambling disorder compared with

64 controls in a task involving images of playing cards (van Holst et al, 2012). Furthermore, the Tsurumi et al. study used ‘points’ in the MID task instead of a financial reward (Tsurumi et al, 2014), and blunted win anticipation responses in this task may represent the relative non- salience of this reward in gambling disorder.

There is one published study comparing alcohol dependent and gambling disorder participants’ MID task responses, but this did not show any significant win>neutral anticipation differences between these two addictive disorders (Romanczuk-Seiferth et al, 2015).

1.9. Combining PET and fMRI imaging

As discussed in Section 1.7.1., the fMRI BOLD signal allows the regional change in the ratio of oxygenated and deoxygenated haemoglobin to be measured as a surrogate for regional brain function. PET imaging can be combined with fMRI measures to examine the associations

15 between BOLD signal and quantitative PET measures of blood flow with [ O]H2O PET and brain metabolism with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) PET (AbdulSabur et al, 2014; Kameyama et al, 2016; Newberg et al, 2005; Rostrup et al, 2000; Wehrl et al, 2013). This aids with the understanding of the underlying physiological processes that result in the fMRI BOLD signal (Buxton, 2013; Ekstrom, 2010).

Similarly, a combination of brain neuroreceptor PET and task-based fMRI allows associations between BOLD responses and a measure of neurotransmission to be examined. This is a relatively novel approach and thus there are few studies combining these PET and fMRI measures (see Table 1.3.). In the few published combined PET/fMRI studies, dopamine is the most investigated neurotransmission system and the MID task is the most commonly used fMRI task. Both higher D2/D3 receptor availability and DAT binding, and MID reward related dopamine release (changes in [11C]raclopride binding) are associated with higher reward anticipation BOLD responses (Asensio et al, 2010; Dubol et al, 2018; Schott et al, 2008;

65 Weiland et al, 2017), suggesting that higher dopaminergic activity is associated with higher financial reward sensitivity.

The next most commonly studied combination of PET and fMRI imaging is the endogenous opioid system and pain. Published studies have shown that higher opioid receptor availability (both MOR and non-specific) is associated with higher painful pressure stimulus-induced BOLD responses (Schrepf et al, 2016; Wey et al, 2014), but lower MOR availability is associated with higher BOLD responses to vicarious pain stimuli (watching videos of other experiencing pain) (Karjalainen et al, 2016).

Table 1.3. – Summary of published studies combining neuroreceptor PET and fMRI imaging. fMRI Study Authors PET tracer Results paradigm population Dopamine (Heinz et al, [18F]desmethoxy- Alcohol cues Alcohol In AD lower ventral 2004) fallypride dependence and striatum D2 receptor healthy controls availability associated with higher alcohol craving and higher alcohol cue BOLD response. (Schott et al, [11C]raclopride MID task Healthy controls Higher NAcc [11C]raclopride 2008) (D2/D3) displacement (i.e. dopamine release) associated with higher MID BOLD win>neutral anticipation contrast. 11 (Asensio et [ C]raclopride Sustained Cocaine Higher caudate D2/D3 al, 2010) (D2/D3) attention task dependence receptor availability with associated with higher monetary thalamic reward BOLD incentive responses. 11 (Weiland et [ C]raclopride MID task Healthy controls In all subjects higher D2/D3 al, 2017) (D2/D3) who are FH+ or displacement associated FH- and high or with higher reward low risk for anticipation BOLD alcohol responses. High risk, FH+ dependence controls had higher striatal D2/D3 displacement during MID task. (Dubol et al, [11C]PE2I MID task Cocaine In all participants higher 2018) (DAT) dependence, midbrain DAT availability schizophrenia, associated with higher win depression and anticipation BOLD healthy controls, responses in striatum.

66 Table 1.3. – Summary of published studies combining neuroreceptor PET and fMRI imaging (continued). Opioid (Wey et al, [11C]diprenorphine Painful Healthy controls Higher pain-induced 2014) (mu, kappa and pressure reductions in delta opioid) stimulus [11C]diprenorphine binding (i.e. opioid release) in left thalamus associated with higher pain-induced BOLD responses. (Schrepf et al, [11C]carfentanil Painful Fibromyalgia Higher MOR availability 2016) (mu opioid) pressure associated with higher pain- stimulus induced BOLD responses. Dopamine and opioid (Karjalainen [11C]carfentanil Videos Healthy controls Primarily negative et al, 2017) (mu opioid) and depicting correlations between MOR [11C]raclopride persons in availability and pain related (D2/D3) painful and BOLD responses. No painless significant association situations between D2/D3 availability and BOLD responses. 5HT/serotonin 11 (Selvaraj et [ C]CUMI-101 Fearful and Healthy controls Higher dorsal raphe 5HT1A al, 2018) (5HT1A) neutral faces availability associated with task with higher citalopram-induced citalopram vs. increases in fearful faces placebo BOLD response. FH-/FH+ – Family history negative and positive

1.9.1. Simultaneous PET/fMRI imaging

The published studies in Table 1.3. used sequential PET and fMRI imaging, where the PET and fMRI scans occurred at different times. Scanners that are capable of simultaneous PET and fMRI imaging have recently become available, and there are a number of human studies examining simultaneous fMRI BOLD signal and regional brain metabolism using FDG PET (Golkowski et al, 2017; Hahn et al, 2017; Marchitelli et al, 2018; Riedl et al, 2014). There are few published studies examining the simultaneous associations between neuroreceptor/transmitter PET and fMRI BOLD signals in humans. One publication studied the interaction between changes in [11C]raclopride binding and brain BOLD connectivity in mothers whilst watching videos of their children (Atzil et al, 2017). Another examined the effect of painful stimuli and placebo on BOLD responses and [11C]diprenorphine binding with

67 simultaneous PET/fMRI but did not examine the association between PET and fMRI measures (Linnman et al, 2018).

There are also a number of technical challenges to simultaneous PET/fMRI, particularly in the use of the structural three-dimensional MRI images in attenuation correction of the PET data (Flavell et al, 2016). A number of studies have shown potential bias in PET outcome measures when using MRI based attenuation correction compared with CT based attenuation correction (Choi et al, 2014; Jena et al, 2014).

1.10. Aims of thesis

This thesis aims to address the following key gaps in the published literature discussed in the introduction. Firstly, whilst a number of studies have examined MOR availability in alcohol dependence during early abstinence (Heinz et al, 2005; Weerts et al, 2011), MOR availability in later abstinence has not been previously been examined. High MOR availability is a possible mediator of craving in alcohol dependence during abstinence (Heinz et al, 2005), and craving and relapse in cocaine dependence (Gorelick et al, 2008; Zubieta et al, 1996). This high MOR availability is a theorised target for opioid receptor antagonist treatment in alcohol dependence (Hermann et al, 2017). If high MOR availability does not persist into longer durations of abstinence, for example the evidence that high MOR availability may ‘normalise’ with longer durations of abstinence in cocaine dependence (Gorelick et al, 2005), then opioid receptor antagonists may be less effective in aiding abstinence in long-term abstinent individuals.

Secondly, it has been shown that there is blunted endogenous opioid release in gambling disorder (Mick et al, 2016), a behavioural addiction, but endogenous opioid tone has not been examined in alcohol dependence. As discussed in Section 1.2. there are important similarities between substance and behavioural addictions, although the findings of higher MOR availability in cocaine and alcohol dependence (Gorelick et al, 2005; Heinz et al, 2005; Weerts et al, 2011; Zubieta et al, 1996) have not been replicated in gambling disorder (Mick et al,

68 2014). Given there is evidence of the effectiveness of opioid receptor antagonists in managing both alcohol dependence (Lingford-Hughes et al, 2012; NICE, 2014) and gambling disorder (Grant et al, 2014), it is important to understand if there is a common dysregulation of endogenous opioid signalling in these two addictive disorders which may underpin a shared mechanism for opioid receptor antagonist treatment in addiction.

Finally, as discussed in Sections 1.3.1., 1.3.2. and 1.3.3. endogenous opioid signalling plays an important role in reward processing (Berridge and Kringelbach, 2015; Le Merrer et al, 2009), and it is hypothesised that the reward and endogenous opioid ‘deficits’ are closely interrelated in addiction (Oswald and Wand, 2004; Ulm et al, 1995) (see Section 1.3.4.). However, it is unclear if the changes in endogenous opioid signalling shown in alcohol dependence (Heinz et al, 2005; Weerts et al, 2008) and other addictions (Gorelick et al, 2005; Mick et al, 2014; Zubieta et al, 1996) are related to altered reward sensitivity (Volkow et al, 2010) or are primarily a mediator of craving in early abstinence, as shown with the associations between craving and MOR availability in alcohol and cocaine dependence (Gorelick et al, 2005; Heinz et al, 2005; Williams et al, 2009; Zubieta et al, 1996). Understanding the associations between reward sensitivity and endogenous opioid signalling in addiction would improve our understanding of the role of dysregulated endogenous opioid signalling in addiction and relapse, and possibly provide a new mechanistic target for treatments in addiction.

The specific aims of this thesis are to characterise the endogenous opioid system in alcohol dependence by measuring MOR availability and oral dexamphetamine-induced endogenous opioid release (i.e. endogenous opioid tone). The associations between MOR availability and endogenous opioid tone and clinical variables in alcohol dependent individuals will also be explored. Finally, the associations between MOR availability and endogenous opioid tone and reward responses (using the MID task) will be explored to understand the link between dysregulated opioidergic signalling and reward in addiction. These aims will be examined in three results chapters and specific hypotheses can be found in each of the three results chapters (Sections 3.1.3., 4.1.3. and 5.1.3.).

69 Aims: 1. Examining differences in MOR availability between healthy controls and abstinent alcohol dependent participants using [11C]carfentanil PET. Associations between MOR availability and clinical variables (e.g. duration of abstinence from alcohol and severity of dependence) will also be examined in alcohol dependent participants.

2. Examining if there are differences in oral dexamphetamine-induced endogenous opioid release between healthy controls and abstinent alcohol dependent participants using [11C]carfentanil PET. Oral dexamphetamine endogenous opioid release in abstinent alcohol dependent participants will also be compared with gambling disorder participants to examine if heavy alcohol use in alcohol dependence has a mediating effect on the blunted endogenous opioid release shown in gambling disorder. Finally, associations between oral dexamphetamine-induced endogenous opioid release and clinical variables (e.g. duration of abstinence from alcohol and severity of dependence) will be examined in alcohol dependent participants.

3. Exploring if there are associations between reward anticipation brain responses and MOR availability or dexamphetamine-induced endogenous opioid release by combining data from the ICCAM MID fMRI task and [11C]carfentanil PET.

70 CHAPTER 2: METHODS

The data in this thesis were collected from five clinical imaging studies conducted between 2010 and 2017. All studies were carried out using the principles from the Declaration of Helsinki and approved by a Research Ethics Committee (REC), and the Administration of Radioactive Substances Advisory Committee, UK. These data were all collected as part of a program using [11C]carfentanil PET to examine MOR availability and dexamphetamine- induced endogenous opioid release at the Neuropsychopharmacology Unit, Imperial College London. This program is briefly outlined below and each study will be discussed in more detail in Section 2.2..

The [11C]carfentanil PET and oral dexamphetamine challenge method was first used by Dr Alessandro Colasanti (Section 2.2.3.) to show endogenous opioid release in healthy controls (Colasanti et al, 2012). These results where reproduced by Dr Inge Mick (Mick et al, 2014) who then used the same method to show blunted endogenous opioid release in gambling disorder participants (Section 2.2.4.) (Mick et al, 2016). Next, the [11C]carfentanil PET dexamphetamine challenge protocol was applied to examine MOR availability and endogenous opioid release in alcohol dependent participants (Section 2.2.1.) which is the primary focus of this thesis. As part of an amendment to the [11C]carfentanil PET in alcohol dependence protocol Dr Abishekh Ashok also investigated if an intravenous acetate challenge could also induce endogenous opioid release in healthy controls (Section 2.2.1.).

As part of the wider program using PET imaging to investigate neurotransmitter systems in addiction at the Neuropsychopharmacology Unit, Dr Inge Mick also carried out a study using [11C]Ro15-4513 to measure GABA-A receptor availability in gambling disorder participants and healthy controls (Section 2.2.5.). Another study examining endogenous GABA release in alcohol dependence using [11C]Ro15-4513 PET and a tiagabine challenge was also started by myself but unfortunately was not completed (Section 2.2.2.).

71 As stated in Section 1.10. the first two primary aims of this thesis were to investigate MOR availability and dexamphetamine induced endogenous opioid release in alcohol dependence. To achieve these aims healthy control data from the entire [11C]carfentanil PET program at the Neuropsychopharmacology unit were used in this thesis to compare with data from alcohol dependent participants. The third primary aim of this thesis was to investigate reward responses in alcohol dependence using the MID fMRI task, and as all [11C]carfentanil and [11C]Ro15-4513 PET studies in gambling disorder and alcohol dependence used the same ICCAM MID task fMRI imaging protocol, data from these studies were combined to address this aim.

Specific details of the participant populations for the studies mentioned above are in Section 2.2.. Further information regarding recruitment, study and scanning procedures and data collection for each of the studies is also given in detail later in the Chapter 2.

2.1. Statement of contribution to the work in this thesis

2.1.1. Data collection

The studies by Dr Colasanti and Dr Mick investigating dexamphetamine induced endogenous opioid release using [11C]carfentanil PET were completed before I started working at the Neuropsychopharmacology Unit in September 2014, as was the [11C]Ro15-4513 PET in gambling disorder study. Therefore, I had no input in any aspects of these studies.

The [11C]carfentanil PET in alcohol dependence study protocol and REC submission were also completed by Dr Inge Mick prior to September 2014, but I was involved in helping to setup the study at the Imanova/Invicro Clinical Imaging Centre (CIC) and the NIHR/Wellcome Trust Imperial Clinical Research Facility, Hammersmith Hospital, London. After the setup of the study I had primary responsibility for recruiting, screening and scanning the 13 alcohol dependent and 5 healthy control participants included in this study. This also included the injection of [11C]carfentanil and providing clinical cover for PET and fMRI scans. However,

72 guidance and support from Prof Anne Lingford-Hughes, Dr Mick, Dr Eugenii Rabiner and other staff at the Neuropsychopharmacology Unit, Imanova/Invicro CIC and NIHR/Wellcome Trust Imperial Clinical Research Facility was invaluable in the running of this study.

I was responsible for writing the protocol and other documents for the [11C]Ro15-4513 in alcohol dependence study, including patient information sheets, and completing the REC for this study. The study design was primarily decided by Profs Anne Lingford-Hughes and David Nutt as part of their MRC Neurotransmitters in Opioid and Alcohol Addiction (NOAA) grant (G1002226), and Prof Lingford-Hughes provided a great deal of advice with the preparation of the study documents and submission to REC as well as attending the REC committee meeting me. For this study I was responsible for recruiting, screening and scanning participants with the help of Dr Claire Durant.

Dr Abishekh Ashok amended the [11C]carfentanil PET in alcohol dependence study protocol and other documents to add the acetate challenge study to the REC approvals. Dr Ashok and Dr Eugenii Rabiner were responsible for the study design and primary hypotheses for this study and Dr Ashok carried out all recruitment, screening and scanning of the acetate challenge participants. My role in this study was contributing comments to the study document amendments and advising Dr Ashok on the recruitment, screening and scanning procedures used in our [11C]carfentanil PET in alcohol dependence study. I also assisted by running the acetate challenge [11C]carfentanil PET study visit for one of the participants in this study.

2.1.2. Hypothesis development

The primary hypotheses for Chapter 3 and Chapter 4 of this thesis (see Sections 3.1.3. and 4.1.3.) regarding differences in MOR availability and endogenous opioid release in alcohol dependence were decided as part of the protocol for the [11C]carfentanil PET in alcohol dependence study. As stated above this protocol was written by Dr Inge Mick and Prof Anne Lingford-Hughes prior to my starting work at the Neuropsychopharmacology Unit in September 2014. The hypotheses in Chapter 3 examining the associations between clinical

73 and demographic variables and MOR availability were developed by myself with advice and guidance from Prof Anne Lingford-Hughes. Similarly, the hypotheses in Chapter 4 examining differences in endogenous opioid release between alcohol dependent and gambling disorder participants, subjective effects of dexamphetamine and associations with clinical variables were developed by myself with advice and guidance from Prof Anne Lingford-Hughes.

The hypotheses in Chapter 5 (see Section 5.1.3.) were developed by myself with advice and guidance from Prof Anne Lingford-Hughes, Dr Jim Myers, Dr Jimmy Lan, Dr Louise Patterson and Dr John McGonigle.

2.1.3. Data analysis

[11C]carfentanil PET and fMRI imaging data used in the analysis in this thesis were provided by Invicro/Imanova anonymised in Neuroimaging Informatics Technology Initiative (NIfTI) file format, along with ancillary data including additional data from PET scans, such as injected activity and mass, and fRMI task E-Prime data.

The [11C]carfentanil PET data were processed using the MIAKAT pipeline by both myself and Dr Jim Myers, and Dr Myers was instrumental in guiding the methodology used for this (more

11 details in Section 2.10.). I then used the [ C]carfentanil BPND ROI output data from the MIAKAT pipeline to examine our hypothesis regarding MOR availability and dexamphetamine induced endogenous opioid release in alcohol dependence. These analyses were guided by the methods used by Dr Colasanti and Dr Mick in their respective published [11C]carfentanil PET dexamphetamine challenge papers (Colasanti et al, 2012; Mick et al, 2014, 2016).

The pre-processing and first level analysis of the raw fMRI data were carried out by myself using an automated pipeline developed by Dr John McGonigle for the ICCAM platform study (see Section 2.13.). Dr Jimmy Lan assisted with this by converting E-prime output files into three column text files for FSL. The subsequent group level MID task analysis was carried out in FSL by myself with advice and support from Dr John McGonigle, Dr Louise Patterson and Dr Jimmy Lan. The analysis plan for combining [11C]carfentanil PET and MID task data was

74 discussed in detail with Prof Anne Lingford-Hughes, Dr John McGonigle, Dr Louise Patterson, Dr Jim Myers and Dr Jimmy Lan, with further assistance from Claire Wilkinson in planning the initial ROI-wise analysis.

Demographic, clinical and questionnaire data were transcribed from paper case report forms onto an anonymised electronic database. The data for the gambling disorder [11C]carfentanil and [11C]Ro15-4513 PET studies was transcribed by Dr Inge Mick. The alcohol dependence [11C]carfentanil PET study demographic and questionnaire data was transcribed by Itamar Levin and Claire Wilkinson, whilst I transcribed the alcohol dependence [11C]Ro15-4513 PET and acetate challenge [11C]carfentanil PET study data used in this thesis. The analysis plan for examining demographic, clinical and questionnaire data was developed by myself with guidance from Prof Anne Lingford-Hughes.

Peripheral blood samples from the gambling studies were collected by Dr Inge Mick. Blood samples from the alcohol dependence studies were collected by me with any processing of samples (e.g. to extract plasma, serum or buffy coat) carried out by Invicro/Imanova CIC staff. Samples were then sent for further analysis, as detailed in Sections 2.8.. The resulting data were then used in analyses as detailed in this thesis, and these analyses were planned by myself with guidance from Prof Anne Lingford-Hughes.

All remaining data analyses presented in this thesis were carried out by myself as described.

2.1.4. Results interpretation

Interpretation of the results in this thesis was carried out myself with the guidance of a number of individuals. [11C]carfentanil PET results were discussed with Dr Jim Myers, Prof David Nutt and Prof Anne Lingford-Hughes, MID fRMI results were discussed with Dr Louise Patterson, Dr Liam Nestor, Dr John McGonigle, Dr Jimmy Lan, Prof David Nutt and Prof Anne Lingford-Hughes and combined fMRI PET results were discussed with Dr Louise Patterson, Dr Jim Myers, Dr Liam Nestor, Dr Jimmy Lan, Dr John McGonigle, Prof David Nutt and Prof Anne Lingford-Hughes.

The interpretation of the results in this thesis was primarily carried out by myself. However, the interpretation has greatly benefitted from the assistance of the colleagues listed above, include advice with improving clarity of the interpretation, suggesting additional literature to better understand the findings, or suggesting changes to analyses or additional analyses that could be carried out.

2.2. Study populations

2.2.1. [11C]carfentanil PET in alcohol dependence: Imaging baseline mu-opioid receptor availability and stimulated release of endogenous opioids in alcohol dependent patients using [11C]carfentanil PET and dexamphetamine challenge (Ethics: 14/LO/1552)

In this study thirteen alcohol dependent participants and five healthy controls received two [11C]carfentanil PET scans; a baseline scan and a scan following an oral 0.5mg/kg dexamphetamine challenge and these data were collected by myself. Participants underwent structural MRI T1 imaging and fMRI imaging consisting of ICCAM MID, go-nogo and evocative images tasks (only the MID task was examined in this thesis). A single healthy control also participated in the [11C]Ro15-4513 PET in gambling disorder study (Section 2.1.5.), and for the purposes of the analysis in this thesis his fMRI data from the [11C]carfentanil PET study in alcohol dependence were used rather than his fMRI data from the [11C]Ro15-4513 study in gambling disorder study.

An additional six healthy controls were scanned by Dr Abishekh Ashok using the same protocol and underwent an intravenous acetate challenge with [11C]carfentanil PET scans pre- and post-acetate challenge. These healthy controls only underwent structural MRI T1 imaging.

76 2.2.2. [11C]Ro15-4513 PET in alcohol dependence: An investigation of the effects of acute Tiagabine administration on GABA-A receptors in Alcohol Dependence using [11C]Ro15-4513 GABA-A PET radioligand (Ethics: 15/LO/1482)

In this study two healthy controls received two [11C]Ro15-4513 PET scans, one before and one after a 15mg oral tiagabine challenge. This study ended early due the end of the MRC NOAA program grant funding this research, and no alcohol dependent participants were recruited before the end of the study. Participants underwent structural MRI T1 imaging and fMRI imaging consisting of ICCAM MID, Go-No-Go and evocative images tasks. These data were collected by myself.

2.2.3. [11C]carfentanil PET in healthy controls: An open label Positron Emission Tomography study in healthy male subjects to characterize [11C]carfentanil, a mu opioid selective agonist radiotracer, in terms of vulnerability to changes in endogenous opioid peptides elicited by acute administration of dexamphetamine and hydrocortisone (Ethics: 09/H0301/80)

The data in this study were collected by Dr Alessandro Colasanti. In this study twelve healthy controls received up to three [11C]carfentanil PET scans. All participants underwent a baseline [11C]carfentanil scan (n=12) and then received either a high (0.5mg/kg, n=6) or ultra-low (~0.017mg/kg, n=6) dose of oral dexamphetamine and then a second [11C]carfentanil scan. An ultra-low dexamphetamine dose was used as the placebo condition to control for any expectation effects of receiving amphetamine without producing pharmacological effects (Colasanti et al, 2012). Participants underwent structural MRI T1 only.

77 2.2.4. [11C]carfentanil PET in gambling disorder: Imaging baseline mu opioid receptor availability and stimulated release of endogenous opioids in pathological gamblers using [11C]carfentanil PET and dexamphetamine challenge (Ethics: 12/LO/0750)

The data in this study were collected by Dr Inge Mick. In this study nine healthy controls and fifteen individuals with gambling disorder received two [11C]carfentanil PET scans; a baseline scan and a scan following an oral 0.5mg/kg dexamphetamine challenge. Participants underwent structural MRI T1 imaging and fMRI imaging consisting of ICCAM MID and go-nogo tasks and gambling cue reactivity task (Limbrick-Oldfield et al, 2017; Mick et al, 2014, 2016).

2.2.5. [11C]Ro15-4513 PET in gambling disorder: An investigation of GABA-A availability in pathological gamblers using the relatively selective α5 GABA-A PET radioligand [11C]Ro15-4513 (Ethics: 12/LO/1279)

The data in this study were collected by Dr Inge Mick. In this study fifteen gambling disorder participants and twenty healthy controls received a single [11C]Ro15-4513 PET scan. A number of these individuals also participated in the dexamphetamine challenge [11C]carfentanil PET in gambling disorder study (eleven gambling disorder participants and eight healthy controls). Participants underwent structural MRI T1 imaging and fMRI imaging consisting of ICCAM MID and go-nogo tasks and gambling cue reactivity task (Limbrick-Oldfield et al, 2017; Mick et al,

2017).

78 Table 2.1. – Summary of the number of individual healthy control (HC), alcohol dependent (AD) and gambling disorder (GD) participants across the five studies with [11C]carfentanil PET and ICCAM MID task data. [11C]carfentanil PET data ICCAM MID Task Study Post 0.5mg/kg Baseline data Dexamphetamine [11C]carfentanil dexamphetamine 11 HC 5 HC 5 HC challenge in alcohol dependence 13 AD 13 AD 13 AD (Section 2.2.1.) [11C]Ro15-4513 tiagabine challenge in alcohol dependence N/A N/A 2 HC (Section 2.2.2.) [11C]carfentanil dexamphetamine challenge in healthy controls 12 HC 6 HC N/A (Section 2.2.3.) [11C]carfentanil dexamphetamine 9 HC 9 HC 9 HC challenge in gambling disorder 15 GD 15 GD 15 GD (Section 2.2.4.) [11C]Ro15-4513 in gambling disorder 11 HC N/A N/A (Section 2.2.5.) 5 GD 32 HC 20 HC 27 HC Totals 13 AD 13 AD 13 AD 15 GD 15 GD 20 GD

2.3. Eligibility criteria

Eligibility criteria were similar for all participants (healthy control, alcohol dependence and gambling disorder) across the five studies. Although it was not a specified inclusion criterion, all participants recruited to these studies were right handed.

2.3.1. General eligibility criteria

- Male (see Section 2.3.5. for details). - Aged between 25 and 60 years old. - Capable of giving written informed consent. - Good comprehension of English language. - No use of any illegal drugs within two weeks prior to commencing the study, or during the study (see Section 2.3.5. for details).

79 - No current use of psychotropic medication or medication that interacts with the opioid system (see Section 2.3.5. for details). - No cardiac pacemaker or other electronic device or ferromagnetic metal foreign bodies that would contraindicate MRI scanning. - No history of a neurological diagnosis that may influence the outcome or analysis of the scan results including stroke, epilepsy, space occupying lesions, multiple sclerosis, Parkinson's disease, vascular dementia, transient ischemic attack. Previous alcohol withdrawal seizures are not an exclusion criterion in alcohol dependent participants. - No history of clinically significant head injury (e.g., requiring medical or surgical intervention) - No current DSM-IV Axis I psychiatric diagnosis (e.g. mood disorders or psychotic disorders) - No abnormal screening electrocardiogram (ECG). - No liver function tests (LFT) at screening above five times the upper normal limit (alkaline phosphatase, aspartate transaminase and alanine transaminase) - No pulse rate at screening greater than 100 beats per minute and/or systolic blood pressure >150 mmHg or <100 mmHg.

2.3.2. Additional healthy control eligibility criteria

- No current or previous history of gambling disorder or alcohol abuse/dependence. No previous other substance abuse/dependence. - No previous diagnosis of anxiety or depressive disorder.

2.3.3. Additional alcohol dependent participant eligibility criteria

- No current or previous history of gambling disorder or other substance abuse/dependence. - Previous diagnosis of anxiety and/or depressive disorder is allowed, but no current diagnosis or treatment.

80 2.3.4. Additional gambling disorder participant eligibility criteria

- No current or previous history of alcohol dependence or other substance abuse/dependence. - Previous diagnosis of anxiety and/or depressive disorder is allowed, but no current diagnosis or treatment.

2.3.5. Additional comments regarding eligibility criteria

11 There is evidence of gender differences in [ C]carfentanil BPND in a number of cortical and subcortical regions (Zubieta et al, 1999) and there is also evidence that sex hormone changes during the menstrual cycle may affect MOR availability (Eckersell et al, 1998; Smith et al, 1998; Zubieta et al, 2002). Therefore, given these concerns and the potential difficulty of matching all female participants to the same period of their menstrual cycle the decision was made to only recruit male participants for the [11C]carfentanil PET dexamphetamine challenge studies.

Illegal drug use within the past 2 weeks investigated by a current drug use history and a multi- panel urine drug testing kit which tested for amphetamines, THC, benzodiazepines, opiates, methadone and buprenorphine.

Participants were required to have not used any psychotropic medication or medication that interacts the with opioid system for a minimum of two weeks. This period of time may be longer at the discretion of the study investigator, for example a drug with a long half-life requiring more than 14 days for elimination after stopping.

Psychiatric history was assessed using the Mini International Neuropsychiatric Interview (MINI-5) (Sheehan et al, 1998) and a clinical history during the screening visit. Current or previous diagnoses of anxiety, depression or another psychiatric disorder were determined as a previous clinical diagnosis (with or without previous pharmacological or nonpharmacological treatment) or if in the clinical opinion of the doctor carrying out the screening session that a participant fulfils the criteria of a current or previous history of a

81 psychiatric disorder. Participants were not required to provide documentation from their General Practitioner (GP) regarding their previous mental and physical health history.

2.4. Recruitment

Recruitment for each study was carried out by the same individuals as outlined in Section 2.1.. Participants were recruited in the following manner:

- Healthy male volunteers were recruited via from an existing database of volunteers, and via posters, newspaper advertisements or e-mail distribution lists. - Alcohol dependent participants were recruited from Central North West London (CNWL) NHS Trust drug and alcohol services, and other associated services (e.g. Addaction, Care Grow Live – CGL and Westminster Drug Project – WDP). Individuals who had participated in previous studies in the Neuropsychopharamocology unit at Imperial College London who consented to being added to a participant database were also contacted. - Gambling disorder participants were recruited from the CNWL NHS Trust National Problem Gambling Clinic.

All potential participants were contacted by telephone to discuss the study and to check that they broadly fulfilled the eligibility criteria before being invited to attend a screening visit.

2.5. Study visits

2.5.1. Screening visit

All participants attended a screening visit prior to enrolment in the study. The majority of the screening visit took place at the NIHR/Wellcome Trust Imperial Clinical Research Facility, Hammersmith Hospital, London. This involved a full psychiatric and physical health history,

82 including a detailed drug and alcohol use history, and gambling history. Physical examination was carried out as well as an ECG, screening blood samples, urine drug screen and alcohol breathalyser. Participants also completed questionnaires during the screening visit (see Section 2.5. for details). Participants in the gambling disorder and alcohol dependence studies (including healthy controls) also completed the ICCAM fMRI imaging protocol on the screening visit if they fulfilled the criteria to be included in the study. See Section 2.11. for more details of the MRI imaging protocol.

The fMRI imaging protocol was never carried out on the same visit as [11C]carfentanil or [11C]Ro15-4513 PET scans to ensure that any fMRI task related changes in neurotransmitters did not affect the PET image data. However, in the acetate challenge study and the dexamphetamine challenge Colasanti et al. study structural T1 MRI scans were sometimes completed on the same visit as the [11C]carfentanil PET scans.

2.5.2. [11C]carfentanil PET scan visit

Individuals who participated in the [11C]carfentanil PET and oral dexamphetamine challenge paradigm received two [11C]carfentanil PET scans on their second study visit. The pre- dexamphetamine scan was carried out in the morning (approximately 09.30), which was be followed by a standardised lunch (sandwich) and then an oral 0.5mg/kg dexamphetamine dose. The second PET scan (i.e. post-dexamphetamine scan) was carried out three hours following the oral dexamphetamine dose (approximately 15.30) (Figure 2.1.). See Section 2.9. for more details regarding the PET imaging protocol.

A number of participants (7 HC, 1 AD and 1 GD) had their 0.5mg/kg oral dexamphetamine challenge and post-dexamphetamine [11C]carfentanil PET scan on a separate study visit due to technical problems with the PET scanner or radiotracer production (see Section 2.9.4. for more details).

A number of healthy controls underwent a baseline [11C]carfentanil PET scan and a second [11C]carfentanil PET scan following either an ultra-low dose of oral dexamphetamine (~0.017

83 mg/kg, n=6) (Colasanti et al, 2012) or an intravenous acetate challenge (n=6). The [11C]carfentanil PET scanning pScreening Visit (Day 1)rotocol was similar with the pre-challenge (baseline) [11C]carfentanil PET scan carried out in the morning (approximatelyfMRI scan including 09.30) and post-challenge structural scan and MID fMRI task PET scan in the afternoon of the same day. For these healthy controls(90 mins) only the baseline (pre- ultra-low dose dexamphetamineEligibility and Consent or pre-acetate) [11C]carfentanil PET scan data were used in Medical Examina)on WIN! this thesis for the comparison of MOR availability between alcohol dependent participants LOSE! and healthy controls. Ques)onnaires NEUTRAL

PET Scanning Visit (Day 2) Pre-dexamphetamine scan Post-dexamphetamine scan (90 mins) (90 mins) 0.5mg/kg oral dexamphetamine

(3hrs pre-scan 2)

Figure 2.1. – Representation of the [11C]carfentanil PET and dexamphetamine challenge study visit completed by healthy controls, alcohol dependent and gambling disorder participants.

2.6. Study questionnaires

Participants were required to complete a number of questionnaires as part of the screening visit in the studies. The questionnaires from which data were used in this thesis are included below.

2.6.1. Beck Depression Inventory II (BDI)

The BDI assesses depressive symptoms with 21 items (for example ‘Guilty Feelings’ and ‘Worthlessness’) and individuals self-rate each item with a score between 0-3 depending on severity of the symptoms (Beck et al, 1996). The BDI was completed in all studies on both the

84 screening visit and the morning of the PET scan visits. For the purposes of this thesis the BDI scores collected on the PET scan study visit are presented and used in correlational analyses

11 with [ C]carfentanil BPND and ∆BPND. BDI data were not recorded for two healthy controls in the Colasanti et al. study and so these data were not available for these participants.

2.6.2. Spielberger Trait Anxiety Scale (STAI) and Spielberger State Anxiety Scale (SSAI)

The STAI measures ‘trait’ anxiety (e.g. anxiety as a personal characteristic) with 20 items (for example ‘I feel nervous and restless’ and ‘I feel like a failure’) which are self-rated on a scale from 1 to 4 where 1 is ‘not at all’ and 4 is ‘very much so’ (Spielberger et al, 1983). This questionnaire was only completed at the screening visit. One healthy control did not complete this questionnaire in the [11C]carfentanil PET in gambling disorder study and so the STAI data were missing for this individual.

The SSAI measures ‘state’ anxiety (i.e. current anxiety at a particular moment in time) using 20 items (for example ‘I feel worried’ and ‘I feel comfortable’) which are self-rated on a scale from 1 to 4 where 1 is ‘not at all’ and 4 is ‘very much so’ (Spielberger et al, 1983). The SSAI was completed at screening, and four times during the PET scanning visit, once before and once after each of the two [11C]carfentanil PET scans.

2.6.3. UPPS-P Impulsivity Scale

The Urgency, Premeditation (lack of), Perseverance (lack of), Sensation Seeking, Positive Urgency (UPPS-P) Impulsive Behaviour Scale examines 5 aspects of impulsivity using subscales. It consists of 59 questions which are self-rated from 1 ‘agree strongly’ to 4 ‘disagree strongly’ (Cyders et al, 2007). This questionnaire was not used in the Colasanti et al. study and therefore UPPS-P data were not available for these healthy controls (12 pre-dexamphetamine and 6 post-dexamphetamine healthy controls [11C]carfentanil PET data). Furthermore, one alcohol dependent participant in the [11C]carfentanil in alcohol dependence study and one

85 healthy control in the [11C]Ro15-4513 in alcohol dependence study did not adequately complete the questionnaire and therefore these participants also did not have UPPS-P data available.

2.6.4. Barrett Impulsivity Scale questionnaire-11 (BIS)

The BIS is a scale measuring attentional, motor and non-planning impulsivity, as well as providing a total impulsivity measure. The BIS consists of 30 items (for example: ‘I plan tasks carefully’ and ‘I do things without thinking’) which are self-rated from 1 ‘Rarely/Never’ to 4 ‘Always’ (Patton et al, 1995). The BIS scale was completed in all studies; however, scores were not recorded for three healthy controls in the Colastanti et al. study, and therefore data were not available for these participants.

2.6.5. Fagerström Test for Nicotine Dependence (FTND)

The FTND is a measure of current nicotine dependence in smokers and consists of 6 self-rated questions related to smoking, for example “How soon after you wake up do you smoke your first cigarette?” with variable options depending on the question (Heatherson et al, 1991). This questionnaire was used in all studies, but was only answered by current smokers, and in one case an individual who used a nicotine vaporizer. There were no current smokers in the Colasanti et al. study and therefore no participants in this study completed the FTND.

2.6.6. Alcohol Urge Questionnaire (AUQ)

The AUQ questionnaire measures aspects of craving and urge to use alcohol and consists of eight items (for example: “All I want to do now is have a drink” and “It would be difficult to turn down a drink this minute”) which are self-rated on a seven-point scale from 1 ‘strongly disagree’ to 7 ‘strong agree’ (Bohn et al, 1995). This questionnaire was only included in the

86 [11C]carfentanil and [11C]Ro15-4513 PET in alcohol dependence studies, and so the data were not available for participants in the other studies.

2.6.7. Severity of Alcohol Dependence Questionnaire (SADQ)

The SADQ is a clinical screening tool used to measure the presence and prevalence of alcohol dependence. It is a twenty item self-report questionnaire (for example: “The day after drinking alcohol, I woke up feeling sweaty”) with a four-point scale ranging from ‘almost never’ to ‘almost always’ (Stockwell et al, 1979). The SADQ specifies that participants complete the questions in relation to a period of heavy drinking in the past six months. Alcohol dependent participants who were abstinent for more than six months were instructed to complete the questionnaire in relation to their most recent period of heavy drinking. This questionnaire was only included in the [11C]carfentanil and [11C]Ro15-4513 PET in alcohol dependence studies, and so the data were not available for participants in the other studies.

2.6.8. Time to Relapse Questionnaire (TRQ)

The TRQ is designed to assess risk of relapse and ‘style’ of relapse in abstinent alcohol dependent individuals. It consists of nine items (for example: “I never know ahead of time if I’m going to start drinking again”) which are rated on a four point scale ranging from ‘false’ to ‘very true’ (Adinoff et al, 2010). This questionnaire was only included in the [11C]carfentanil and [11C]Ro15-4513 PET in alcohol dependence studies, and so data were not available for participants in the other studies.

2.6.9. Simplified Amphetamine Interview Rating Scale (SAIRS)

The SAIRS is a simplified version of the Amphetamine Interview Rating Scale (Van Kammen and Murphy, 1975) and was designed to assess the subjective effects of the dexamphetamine.

87 It consists of four items: Euphoric “I feel good”, Alert “I feel energetic”, Restless “I feel like moving” and Anxious “I feel anxious”. Each item is rated on a 10-point scale from 1 “Least ever felt” to 10 “Most ever felt” (Laruelle et al, 1995). SAIRS questionnaires were completed by all participants who received an oral dexamphetamine dose in the [11C]carfentanil PET studies (20 healthy controls, 13 alcohol dependent and 15 gambling disorder participants). SAIRS data were collected at the following timepoints: pre-dexamphetamine (baseline) and 60, 120, 180 and 270 minutes following the dexamphetamine dose.

2.7. Other clinical measures

2.7.1. Harmful alcohol use and abstinence duration

As part of the clinical interview on the screening visit, all participants completed a thorough drug and alcohol history as well as retrospective interview of their drug and alcohol use; a ‘timeline follow-back’. During the drug and alcohol timeline follow-back the interviewer recorded alcohol and drug use (including nicotine) for each year of the participants life, including information on frequency and quantity of use. The clinical interview and timeline follow-back data were used to examine the cumulative harmful effect of alcohol use and duration of abstinence in alcohol dependent participants.

Total lifetime alcohol use is one possible measure to examine the harmful effect of alcohol. However, an older individual with a long period of lower alcohol consumption may have a similar total lifetime alcohol use as a younger individual with a much higher alcohol consumption over a shorter period. Higher alcohol use is more harmful to physical health than low alcohol use (Witkiewitz et al, 2017), and therefore the decision was made to use a measure that would estimate the duration of periods of ‘high risk‘ alcohol use, rather than simply the total lifetime alcohol use. The World Health Organisation (WHO) classifies alcohol use with a ‘high risk’ of physical health consequences in males as >60 grams of alcohol per day (Witkiewitz et al, 2017). Using the timeline follow-back data it was estimated for each

88 participant the total number of lifetime weeks during which they consumed an average of >60g alcohol per day.

2.8. Blood sampling for cortisol, dexamphetamine and genotype data

2.8.1. Plasma dexamphetamine concentration data

Plasma dexamphetamine samples were used to examine the pharmacokinetics of the oral 0.5mg/kg dose. These samples were collected for all alcohol dependent participants (n=13) and for the 14 healthy controls who participated in the [11C]carfentanil in alcohol dependence and gambling disorder studies (see Section 2.1.).

Five plasma dexamphetamine concentration samples were collected in each participant from a canula in the antecubital vein at the following time points: prior to the 0.5mg/kg oral dexamphetamine dose and 60, 120, 180 and 270 minutes following the dexamphetamine dose (see Figure 2.2.).

Venous samples were collected in 10ml EDTA (ethylenediaminetetraacetic acid) tubes and gently inverted several times before being placed in a light proof container with ice. Samples were centrifuged within 30 minutes of collection at 4oc and 1600 G for 15 minutes. Plasma was then pipetted into screw-capped polypropylene tubes for storage in a -80oC freezer. Processing of these samples was carried out by staff at Imanova/Invicro.

Plasma samples were then transferred to the Drug Control Centre, Analytical and Environmental Sciences, King’s College London, UK for analysis by Dr. Alan Brailsford and Dr. Mark Parkin using Gas Chromatography Mass Spectroscopy (GC-MS).

89 2.8.2. Serum cortisol concentration data

Serum cortisol samples were used to examine the serum cortisol response following the oral 0.5mg/kg dexamphetamine challenge. Serum cortisol samples were available for all 13 alcohol dependent participants and the five healthy controls who participated in the [11C]carfentanil in alcohol dependence study (see Section 2.1.).

13 venous samples were collected for serum cortisol concentrations during the dexamphetamine challenge PET visit at the following timepoints: 180, 150, 120, 90, 60 and 30 minutes and immediately prior to dexamphetamine dose and 30, 60, 90, 120, 150 and 180 minutes following the dexamphetamine dose. For the purposes of examining the effect of dexamphetamine on serum cortisol concentration only the sample immediately prior to dexamphetamine dose and the six post-dexamphetamine dose samples were examined (total of seven samples - see Figure 2.2.).

Venous samples were collected from a canula in the antecubital vein in a 5ml SST (serum- separating) tube and gently inverted before leaving to stand upright for 20 minutes. Samples were then centrifuged at 1300 G for 10 minutes at 20oC before pipetting the serum into screw-capped polypropylene tubes for storage in a -80oC freezer. Frozen samples were transferred to the Pathology Department, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK for analysis using the ARCHITECT cortisol assay. This is a chemiluminescent microparticle immunoassay (CMIA) for quantifying cortisol in human serum, plasma or urine on the ARCHITECT iSystem, and is used for routine analysis of clinical samples. The ARCHITECT Cortisol assay has a precision of ≤ 10% total for serum samples ≥ 3 to ≤ 35 μg/dL (http://www.ilexmedical.com/files/PDF/Cortisol_ARC.pdf).

90 C Serum CorEsol D Plasma Dexamphetamine

Minutes: 0 30 60 90 120 150 180 210 250 270

C C C C C C C D D D D D

Oral Dexamphetamine Post-Dexamphetamine Dose (0.3mg/kg) [11C]carfentanil PET scan

Figure 2.2. – Collection timings of plasma dexamphetamine and serum cortisol samples: following oral dexamphetamine dose (0 mins) until end of post-dexamphetamine [11C]carfentanil PET scan (270 mins).

2.8.3. OPRM1 genotype sampling and analysis

Blood samples for genotyping were collected in the [11C]carfentanil and [11C]Ro15-4513 PET studies in gambling disorder and alcohol dependence. Genotype data were collected for healthy controls, alcohol dependent and gambling disorder participants. All participants consented to genetic testing prior to entering these studies. Venous blood samples were collected on the screening visit in 5ml ethylenediaminetetraacetic acid (EDTA) tubes. Following collection, the samples were centrifuged at 1300g, 4°C for 10mins, and the buffy coat extracted and transferred to plastic storage tubes before storage in a -80°C freezer. Processing of these samples was carried out by staff at Imanova/Invicro.

The genotyping analysis was carried out by LGC Limited (Middlesex, UK). DNA was extracted and normalised and underwent SNP-specific KASPTM Assay mix (https://www.lgcgroup.com/products/kasp-genotyping-chemistry). Loci with a call rate less than 90% were not included. Participants were categorised as a G-allele carrier (G:A or G:G) or not (A:A).

91 Genotype samples were collected from a total of 27 healthy controls, 13 alcohol dependent and 20 gambling disorder participants. However, it was not possible to obtain OPRM1 genotype data using the KASPTM assay for one healthy control and one gambling disorder participant.

2.9. Data analysis

2.9.1. Software packages

Data were analysed using the following software packages: - SPSS Mac version 24 (IBM Corp, Armonk, NY, USA) - Excel Mac version 15 (Microsoft, Redmond, WA, USA) - Matlab Mac version 2017a (Mathworks, Natick, MA, USA) - MIAKAT (v1.0 – www.maikat.org) - Statistical Parametric Mapping version 12 (SPM12 – www.fil.ion.ucl.ac.uk/spm) - FMRIB Software Library version 5.0.6 (FSL - https://fsl.fmrib.ox.ac.uk/fsl/fslwiki)

Figures were made using PRISM Mac version 7 (GraphPad Software, La Jolla, CA, USA) and whole-brain figures were made using MRIcron (May 2016 release, McCausland Center for Brain Imaging, University of South Carolina, SC, USA) and are accompanied with MNI 152 template coordinates for coronal, sagittal and axial views.

2.9.2. Statistical analyses

All data analysed in SPSS (Mac version 24) were assessed for normality of distribution using Shapiro–Wilk tests. Data presented in this thesis were normally distributed and analysed with SPSS parametric statistics (e.g. t-tests, ANOVA and Pearson’s correlation) unless otherwise specified. Non-normally distributed data were analysed using non-parametric statistics (e.g.

92 Mann-Whitney-U and Spearman’s correlation). Details of which statistic was used for each analysis is presented in individual results chapters.

Where repeated-measures (within-subject) ANOVA statistics were carried out, data were tested for violations of sphericity using Mauchly's sphericity test. All data that violated sphericity were corrected using Greenhouse-Geisser correction, and the corrected degrees of freedom, F value and p value are presented for these data.

2.9.3. Corrections for multiple comparisons

All non-imaging data were corrected for multiple comparisons using Bonferroni correction.

Both the Bonferroni corrected significance threshold p value and the uncorrected p value are presented for these data.

ROI based PET and fMRI data were also corrected for multiple comparisons using the P-plot Hochberg method. Some multiple comparison correction methods such as Bonferroni assume each comparison is a separate null hypothesis and therefore may be too conservative for use in multiple ROI data where there is a large degree of interrelation of measures between ROIs. The P-plot graphical method is used to estimate of the number of ‘true’ null hypotheses in the data (Turkheimer et al, 2001). Once the true number of null hypotheses has been generated with the P-plot graphical method, this number is then used in the Benjamini- Hochberg false discovery rate (FDR) procedure with an FDR (alpha) of 0.05 (Benjamini and Hochberg, 1995; Turkheimer et al, 2001).

93 2.10. [11C]carfentanil PET Imaging data

2.10.1. [11C]carfentanil PET data collection

[11C]carfentanil PET data collection methods were consistent across the studies in healthy controls, alcohol dependent and gambling disorder participants. All [11C]carfentanil PET data were collected on the same Siemens HiRez Biograph 6 PET/CT scanner (Siemens Healthcare, Erlangen, Germany). Subjects were positioned in the PET scanner and a head-fixation device (soft strap) was used to minimise head movements during the scan. Additionally, each participant’s head motion was monitored by video camera on a screen. Prominent facial features were marked on the video screen for each participant (e.g. nose and eyebrows) and if a participant moved during the scan they were asked to reposition their head using the landmarks on the screen as guidance for position.

A low dose CT scan was performed immediately prior to each PET scan for attenuation and scatter correction of the data. Participants were then injected with up to 350 MBq [11C]carfentanil in 20ml saline, infused over 20 seconds through a canula in the antecubital vein. The cold mass of carfentanil was limited to a maximum of 0.3 μg/kg.

Dynamic [11C]carfentanil emission data were collected continuously for 90 minutes and consisted of 26 frames: 8 × 15 seconds, 3 × 60 seconds, 5 × 120 seconds, 5 × 300 seconds and 5 × 600 seconds.

2.10.2. [11C]carfentanil PET data processing

[11C]carfentanil listmode data were reconstructed by the Siemens HiRez Biograph 6 PET/CT scanner using filtered back projection (discrete inverse Fourier transform, with a 128 matrix, a zoom of 2.6 and a 3-dimentional transaxial Gaussian filter of 5 mm, full width at half maximum – FWHM). This included corrections for scatter, attenuation, dead-time, random detections and radioactive decay.

Dynamic PET image data analysis was carried out by MIAKAT, which also utilises SPM12 (Statistical Parametric Mapping - www.fil.ion.ucl.ac.uk/spm) for linear and non-linear registration. Individual frames were corrected for radioactive decay and corrected for head motion using rigid-body co-registration with the 16th frame as a reference. Each participant’s summed PET image was then rigid-body co-registered to a volumetric T1-weighted magnetisation-prepared rapid acquisition gradient-echo sequence (MPRAGE) MRI (See Section 2.11. for more detail of collection of MRI data).

Each subject’s T1 structural MRI was used to derive the non-linear transformation parameters of the stereotaxic Clinical Imaging Centre (CIC) atlas. The CIC atlas is a hierarchical atlas of 119 regions developed at the GlaxoSmithKline Clinical Imaging Centre (now Imanova/Invicro) (Tziortzi et al, 2011) on the Montreal Neurological Institute (MNI) 152 non-linear 6th generation average brain template (Grabner et al, 2006). The CIC atlas was used in the Colasanti et al. and Mick et al. [11C]carfentanil PET publications in healthy controls and gambling disorder participants (Colasanti et al, 2012; Mick et al, 2014, 2016).

The CIC atlas was non-linearly transformed from MNI 152 space to each participant’s PET data space to extract TAC data for each ROI (see Section 2.9.3. for more detail on non-linear transformation methods). A single TAC was generated for each ROI within the CIC atlas and then regional specific [11C]carfentanil binding was estimated using SRTM (Hirvonen et al, 2009; Lammertsma and Hume, 1996) with an eroded occipital lobe white matter mask as the reference tissue (Figure 2.3.).

Figure 2.3. – Eroded occipital lobe white matter mask used as reference region for [11C]carfentanil SRTM model (MNI152 template space: z6)

11 BPND is the non-displaceable uptake of the radioligand (in this case [ C]carfentanil) It is calculated as the ratio at equilibrium of specifically bound radioligand to that of non- displaceable radioligand in tissue (Innis et al, 2007):

0./ ,(2(34 ,-./ = &/

Where:

0./ is the free fraction in non-displaceable compartment

,(2(34 is the density of receptors available to bind radioligand in vivo

&/ is the dissociation constant

2.10.3. Non-linear co-registration methods in [11C]carfentanil PET analysis

To ensure correct fit of the CIC atlas to individual participants’ structural T1 MRI and PET space, a visual inspection of fit was carried out. The non-linear co-registration method in MIAKAT v1.0 uses FMRIB FSL Brain Extraction Tool (BET) on structural T1 MRIs and then SPM8 Normalisation for non-linear co-registration of the MNI152 template to individual participant

96 space. However, it was found that this method was failing to adequately co-register the MNI 152 template to some participants’ structural T1 scans and this was most apparent in individuals with a large degree of cerebral atrophy (see Figure 2.4. for an example). There was also evidence that this lack of accurate non-linear registration of the MNI 152 template was

11 affecting [ C]Carfentanil BPND data as well, for example some individuals had negative BPND values in regions expected to be high binding (e.g. caudate).

To address this issue, another method of non-linear registration of the MNI 152 template to individual participants’ structural MRI scan was used (implemented into the MIAKAT pipeline by Dr Jim Myers): SPM12 Unified Segmentation. To evaluate if there was a change in

11 [ C]Carfentanil BPND values using SPM12 Unified Segmentation compared with SPM8

11 Normalisation, [ C]Carfentanil BPND were calculated for 10 ROIs (see Table 2.4. for list of ROIs) using both methods (SPM8 Normalisation data were processed by myself and SPM12 Unified Segmentation data were provided by Dr Jim Myers). A mixed model ANOVA was used to assess the within-subject effects of Registration (SPM8 Normalisation or SPM12 Unified Segmentation) and ROI (10 regions) and between-subject effects of Status (healthy control or

11 alcohol dependent) on [ C]Carfentanil BPND. This showed significant within-subject effects of Registration and ROI, and between-subject effects of Status, and a significant Registration x

ROI interaction (Table 2.2.). Mean BPND values for each registration method across 10 ROIs are presented in Table 2.4.. This suggests that using different non-linear registration methods

11 has a significant effect on [ C]Carfentanil BPND values.

To further examine the significant within-subject effect of Registration, two further repeated measures ANOVAs were run to examine the within-subject effects of ROI (10 regions) and Registration (SPM8 Normalisation or SPM12 Unified Segmentation) in healthy controls and alcohol dependent participants separately. These showed significant within-subject effects of Registration and ROI in both healthy controls and alcohol dependent participants, and there was also a significant Registration x ROI interaction in healthy controls (Table 2.3.).

97 Table 2.2. – Mixed model ANOVA examining the within-subject effects of ROI (10 regions) and Registration (SPM8 Normalisation or SPM12 Unified Segmentation) and between-subject effect of Status (healthy

11 control or alcohol dependent) on [ C]Carfentanil BPND in 32 healthy controls and 13 alcohol dependent participants. Effect F-ratio (effect df, error df) p value Registration 17.5 (1.0, 43.0) <0.001 Registration x 2.4 (1.0, 43.0) 0.130 Status ROI 277.0 (4.1, 175.7) <0.001 Within-subject factors ROI x Status 1.8 (4.1, 175.7) 0.133 Registration x 3.5 (2.2, 94.2) 0.030 ROI Registration x 0.8 (2.2, 94.2) 0.482 ROI x Status Between-subject factors Status 4.8 (1, 43) 0.034

Table 2.3. – Repeated measures ANOVA examining the within-subject effects of ROI (10 regions) and

11 Registration (SPM8 Normalisation or SPM12 Unified Segmentation) on [ C]Carfentanil BPND in 32 healthy controls and 13 alcohol dependent participants. Effects F-ratio (effect df, error df) p value Healthy controls Registration 9.3 (1.0, 31.0) 0.005 ROI 281.2 (4.4, 135.8) <0.001 Within-subject factors Registration x 4.2 (1.7, 54.1) 0.024 ROI Alcohol dependent participants Registration 6.1 (1.0, 12.0) 0.030 ROI 75.2 (2.8, 34.1) <0.001 Within-subject factors Registration x 1.2 (2.5, 30.3) 0.330 ROI

Paired sample t-tests were then carried out in healthy controls and alcohol dependent participants to examine in which regions there are significant differences in [11C]Carfentanil

BPND values between the two non-linear registration methods (SPM8 Normalisation vs.

11 SPM12 Unified Segmentation). These showed significantly higher [ C]Carfentanil BPND values in the putamen and anterior cingulate in both healthy controls and alcohol dependent participants using SPM12 Unified Segmentation compared with SPM8 Normalisation (Table 2.4.).

98 In healthy controls there were additional regions; insula, NAcc and amygdala with significantly

11 11 higher [ C]Carfentanil BPND and the thalamus with significantly lower [ C]Carfentanil BPND using SPM12 Unified Segmentation compared with SPM8 Normalisation. In alcohol

11 dependent participants the cerebellum had significantly higher [ C]Carfentanil BPND using SPM12 Unified Segmentation compared with SPM8 Normalisation (Table 2.4.).

11 Table 2.4. – Mean (±SD) [ C]Carfentanil BPND in 10 ROIs comparing SPM8 Normalisation and SPM12 Unified Segmentation registration methods in 32 healthy controls and 13 alcohol dependent participants, including p value from paired-sample t-test comparing registration methods. Healthy Controls Alcohol Dependent ROI SPM12 SPM12 SPM8 Norm p value SPM8 Norm p value Unified Seg. Unified Seg. Hypothalamus 1.78 (±0.50) 1.86 (±0.34) 0.153 1.46 (±0.56) 1.57 (±0.41) 0.279 Caudate 1.46 (±0.27) 1.39 (±0.29) 0.100 1.23 (±0.57) 1.30 (±0.26) 0.515 Putamen 1.80 (±0.22) 1.85(±0.23) <0.001* 1.77 (±0.14) 1.83 (±0.16) 0.001* Thalamus 1.72 (±0.19) 1.68 (±0.19) 0.001* 1.58 (±0.21) 1.58 (±0.17) 0.932 Insula 1.46 (±0.19) 1.48 (±0.18) 0.014* 1.43 (±0.12) 1.43 (±0.13) 0.524 NAcc 2.70 (±0.42) 2.79 (±0.32) 0.018* 2.45 (±0.47) 2.64 (±0.31) 0.066 Frontal Lobe 0.86 (±0.14) 0.86 (±0.15) 0.543 0.79 (±0.13) 0.83 (±0.09) 0.045 Anterior 1.39 (±0.22) 1.47 (±0.20) <0.001* 1.26 (±0.14) 1.37 (±0.11) 0.002* Cingulate Cerebellum 0.71 (±0.23) 0.74 (±0.24) 0.025 0.63 (±0.18) 0.68 (±0.16) 0.009* Amygdala 1.64 (±0.23) 1.72 (±0.21) <0.001* 1.48 (±0.23) 1.57 (±0.19) 0.019 (Bonferroni corrected significance threshold p<0.005, *P-plot Hochberg corrected significant test).

There was also visual evidence of better fit of the MNI 152 template and CIC atlas to individual participants’ structural MRI using SPM12 Unified Segmentation compared with SPM8 Normalisation (Figure 2.4.).

Figure 2.4. – Non-linear co-registration of MNI 152 template to subject space comparing between MNI SPM8 Normalisation (left image) and SPM12 Unified Segmentation (right image) in a single subject (structural T1 central image)

11 Finally, the negative [ C]Carfentanil BPND values that were present in some individuals’ high binding regions using SPM8 Normalisation were no longer negative when using SPM12

11 Unified Segmentation, and were within the range of other participant’s [ C]Carfentanil BPND values. Based on these results the decision was made to use SPM12 Unified Segmentation as

11 the non-linear registration technique for the [ C]Carfentanil BPND analysis.

2.10.4. ROI volume in healthy controls and alcohol dependent participants

To further assess the potential issues with atrophy and examine if there were differences in brain volume in alcohol dependent participants compared with healthy controls, ROI volume sizes (in mm3) were extracted for the 10 ROIs in Table 2.4.. These data were available for all CIC atlas ROIs for each subject as an output from the MIAKAT pipeline. Table 2.5. shows that alcohol dependent participants had lower mean volume in 8 out of 10 ROIs which only had an independent sample t-tests p<0.05 in the NAcc, although this did not survive correction for multiple comparisons. Figure 2.5. also shows that the range of ROI volumes was not dissimilar between alcohol dependent participants and healthy controls in example ROIs.

100 Table 2.5. – Mean (±SD) volume (mm3) of 10 ROIs in 32 healthy controls and 13 alcohol dependent participants, including p value from paired-sample t-test comparing volumes between the two participant groups. ROI Volume (mm3 ±SD) ROI Alcohol p value Healthy Controls Dependent Hypothalamus 726 (±117) 706 (±110) 0.605 Caudate 6637 (±863) 6703 (±1366) 0.848 Putamen 9880 (±1027) 9298 (±1048) 0.094 Thalamus 21500 (±1697) 20606 (±1524) 0.107 Insula 17741 (±1636) 17362 (±992) 0.442 NAcc 2290 (±254) 2094 (±183) 0.015 Frontal Lobe 398181 (±29839) 382265 (±34827) 0.130 Anterior 50775 (±515) 48511 (±4081) 0.165 Cingulate Cerebellum 160150 (±11520) 158574 (±12591) 0.687 Amygdala 4991 (±364) 5050 (±489) 0.657 (Bonferroni corrected significance threshold p<0.005)

30000 Healthy Control Alcohol Dependent 25000 )

³ 20000

15000

ROI size (mm 10000

5000

0 Caudate Putamen Thalamus Insula Figure 2.5. – Volumes (mm3) of 4 ROIs in 32 healthy controls and 13 alcohol dependent participants

The associations between MOR availability and ROI volume were also explored in both alcohol dependent and healthy controls using Pearson’s correlation coefficient (Table 2.6.). This did

11 not show any significant correlations between [ C]carfentantil BPND and volume in any of the 10 ROIs.

101 11 3 Table 2.6. – Pearson’s R values for correlations between [ C]carfentantil BPND and volume (mm ) in 10 ROIs in 32 healthy controls and 13 alcohol dependent participants Pearson’s R value ROI Alcohol Healthy Controls Dependent Hypothalamus 0.210 -0.558 Caudate -0.114 -0.099 Putamen -0.366 -0.365 Thalamus 0.060 0.447 Insula 0.168 0.228 NAcc 0.217 -0.103 Frontal Lobe -0.233 0.231 Anterior 0.053 0.071 Cingulate Cerebellum 0.043 -0.127 Amygdala -0.139 0.130 (No p-values meet Bonferroni corrected significance threshold p<0.005)

2.10.5. Different day pre- and post-dexamphetamine PET scans

The effect of having pre- and post-dexamphetamine [11C]carfentanil PET scans on different days was examined in healthy controls. An independent sample t-test showed no significant

11 (p=0.312) differences in NAcc [ C]carfentanil ∆BPND compared between healthy controls who had their [11C]carfentanil PET scans on the same day and on different days. As can be seen in

11 Figure 2.6., [ C]carfentanil ∆BPND values are similarly distributed between the groups of healthy controls who had their [11C]carfentanil PET scans on the same and different days.

Healty Controls Figure 2.6. – Individual NAcc 0.2 11 Alcohol Dependent [ C]carfentanil ∆BPND values ND

0.1 (including line for mean values) in healthy controls and alcohol 0.0 dependent participants who had C]carfentanil ∆BP

¹¹ 11 -0.1 their [ C]carfentanil PET scans on

NAcc [ the same (13 HC, 12 AD) and -0.2 Same Diﬀerent different days (7 HC, 1 AD). Day Day

102 2.10.6. Examination of intra-scan head motion before and after dexamphetamine challenge

As described in Section 2.10.2. PET image frames 1-15 and 17-26 were rigid body co-registered to frame 16. The rigid body co-registration produced realignment parameters for movement in the x, y and z planes (in mm) and yaw, pitch and roll rotation (in degrees) and these were output by the MIAKAT pipeline for each frame in relation to frame 16. To examine if there was an effect of dexamphetamine on motion during the scan, realignment parameters where used to calculate the mean inter-frame motion for x, y and z planes and yaw, pitch and roll rotation for each subject. These values where then used to examine if there was a significant change in head motion following the dexamphetamine challenge in alcohol dependent participants or healthy controls and if there was a significant difference in head motion compared between healthy controls and alcohol dependent participants (see Table 2.7.).

Table 2.7. – Mean inter-frame head motion parameters (X,Y and Z in mm and Roll, Pitch and Yaw in degrees) during pre- and post-dexamphetamine [11C]carfentanil PET scans in healthy controls and alcohol dependent participants. Head motion Healthy Controls Alcohol Dependent component Pre- Post- Pre- Post- dexamphetamine dexamphetamine dexamphetamine dexamphetamine X 0.20 (±0.07) 0.22 (±0.09) 0.24 (±0.09) 0.27 (±0.10) Y 0.19 (±0.08) 0.21 (±0.09) 0.20 (±0.12) 0.20 (±0.10) Z 0.30 (±0.10) 0.31 (±0.10) 0.28 (±0.09) 0.31 (±0.07) Roll 0.33 (±0.14) 0.41 (±0.22) 0.39 (±0.20) 0.48 (±0.26) Pitch 0.29 (±0.13) 0.28 (±0.11) 0.28 (±0.13) 0.34 (±0.19) Yaw 0.25 (±0.17) 0.27 (±0.13) 0.25 (±0.12) 0.27 (±0.09) (All paired and independent sample t-tests uncorrected p>0.05)

Paired sample t-tests examining changes in the six motion parameters during the post- dexamphetamine scan compared with the pre-dexamphetamine scan were not significant in healthy controls or alcohol dependent participants. Independent sample t-tests did not show any significant differences in motion parameters in alcohol dependent participants compared with healthy controls during either pre- or post-dexamphetamine scans.

103 2.11. Selecting regions of interest for [11C]carfentanil PET data analysis

2.11.1. Selecting high [11C]Carfentanil binding regions

The published Mick et al. study using the [11C]carfentanil PET dexamphetamine challenge

11 protocol examined differences in [ C]carfentanil BPND between healthy controls and gambling disorder participants in ten high [11C]carfentanil binding ROIs (Mick et al, 2016). However, given the current dataset had a larger number of participants, I decided to extend the number of ROIs used to explore differences in MOR availability between healthy controls and alcohol dependent participants.

11 There are a total of 269 ROIs in the CIC atlas for which TACs and [ C]carfentanil BPND data were generated in the MIAKAT pipeline. This consisted of hierarchical regions starting with gross structures, for example cortical and subcortical ROIs, followed by progressive levels of division to individual structures, for example putamen or thalamus, and in some cases subdivision of these smaller structures. An example of this hierarchical structure is shown in Figure 2.7..

‘Sub-cor)cal’ ‘Basal ganglia’ ‘Striatum’ ‘Putamen’

‘Precomissural Putamen’

‘Dorsal Precomissural Putamen’

Figure 2.7. – Illustrated example of the hierarchical structure of the regions in the CIC atlas.

11 At each level of the regional hierarchy [ C]carfentanil BPND values were also generated for bilateral and unilateral ROIs resulting in three BPND values for each ROI: left, right and

104 combined left and right. By only including bilateral ROIs a total of 103 hierarchical ROIs remained.

To ensure that the same areas of the brain were not included in more than one of the ROIs used in the analysis either subdivisions of a ‘parent’ region, or the parent region were selected. For example, selecting either the parent region ‘Putamen’ or subdivisions ‘Precommissural Putamen’ and ‘Poscommissural Putamen’ but not both to ensure that the same area of brain was not repeated in more than one of the ROIs examined. In cortical areas there were some regions where subdivisions of the lobe may be of interest, for example in the frontal lobe where the orbital frontal cortex has been examined with [11C]carfentanil PET in relation to alcohol-induced endogenous opioid release, and there is evidence of differences

11 in [ C]carfentanil BPND in subdivisions including the dorsolateral pre-frontal cortex (DLPFc) in addiction (Bencherif et al, 2004b; Gorelick et al, 2005). Whilst in other cortical regions, such as the parietal lobe, subdivisions are of less interest in relation to MOR availability in addiction. Therefore, the frontal lobe for was subdivided for ROI selection whilst the parietal lobe was not subdivided. The temporal lobe was subdivided into hippocampus and amygdala and the remaining temporal lobe.

In subcortical regions the same level of subdivision to that used in the Colasanti et al. and Mick et al. publications using [11C]carfentanil PET and dexamphetamine challenge. For example, NAcc, putamen and caudate rather than the larger ‘striatum’ ROI or subdivisions of these regions such as ‘Precommissural Putamen’ and ‘Poscommissural Putamen’ (Colasanti et al, 2012; Mick et al, 2014, 2016). The Pallidum was subdivided into ‘Globus Pallidus’ and ‘Ventral Pallidum’ due to the extensive literature of the key role of the ventral pallidum in the opioid modulation of reward and hedonic behaviours (Berridge and Kringelbach, 2015). The occipital cortex was not included due to its use as the reference region in the [11C]carfentanil SRTM model. 22 ROIs were selected from the list of hierarchical regions and these ROIs are listed in Table 2.8.

105 11 Table 2.8. – List of 22 CIC atlas ROIs to be assessed for [ C]carfentanil BPND Location ROIs Cortical Insula, Temporal Lobe, Hippocampus, Amygdala, Precentral Gyrus, Dorsolateral prefrontal cortex (DLPFc), Medial prefrontal cortex (MPFc), Frontal operculum, Orbitofrontal cortex (OFc), Supplementary motor area (SMA), Posterior Cingulate, Anterior Cingulate and Parietal Lobe Subcortical Globus Pallidus, Ventral Pallidum, Caudate, Nucleus Accumbens (NAcc), Putamen, Thalamus, Hypothalamus Other Cerebellum, Brain Stem

The decision was made in discussion with a PET methodologist (Dr Jim Myers) to only include regions with high MOR availability. There are a number of published methods in the literature

11 for thresholding [ C]carfentanil BPND values to select ROIs or voxels with a reasonable level of specific binding for use in analysis. However, there is no consistency in the literature, with ranges of voxel-wise BPND > 0.1 to ROI-wise BPND >1.1 (Harris et al, 2009; Scott et al, 2008;

Wager et al, 2007). Other studies have applied a threshold for voxels with BPND values >1.2 to

>1.3 times the whole-brain average BPND (Scott et al, 2007a; Zubieta et al, 2003b). In previous analyses examining the effect of oral dexamphetamine challenge ROIs have been used with

11 [ C]carfentanil BPND ranges from 0.8 in the cerebellum to 2.8 in the NAcc (Mick et al, 2016).

Therefore, a ROI-wise BPND threshold of 0.5 was chosen.

11 Mean [ C]Carfentanil BPND values were calculated for the 22 ROIs listed in Table 2.8. using data from 32 healthy control participants. Of the 22 ROIs, only the brainstem (BPND 0.23) had

11 a mean [ C]Carfentanil BPND value <0.5. The remaining 21 regions (all BPND >0.5) were

11 included for [ C]Carfentanil BPND analysis in this chapter.

2.11.2. Grey matter masks

11 The atlas ROIs selected for the [ C]Carfentanil BPND analysis in healthy controls and alcohol dependent participants in Section 2.10.1. includes both cortical and subcortical regions. When examining the subcortical regions, the ROIs cover clearly demarcated anatomical area

11 containing voxels with relatively high [ C]Carfentanil BPND values. However, the atlas ROIs covering large cortical regions, for example cerebellum, temporal lobe and frontal regions,

11 contain areas of white matter with relatively low [ C]Carfentanil BPND values (Figure 2.8.). To

106 11 address this issue of low [ C]Carfentanil BPND value white matter areas in the large cortical

11 ROIs a grey matter mask was used for the modelling of [ C]Carfentanil BPND within these

11 regions. The [ C]Carfentanil BPND values from the analysis using a grey matter mask in these

11 cortical regions was compared to [ C]Carfentanil BPND values without a grey matter mask using paired sample t-tests in 32 healthy control participants. This showed significantly higher

11 [ C]Carfentanil BPND values when using the grey matter mask in all regions (Table 2.9.). Therefore, the decision was made to use grey matter masked data for these ROIs.

BPND 0 1 2 3

11 Figure 2.8. – Cerebellum ROI mask overlaid on T1 image in MNI 152 space and mean [ C]Carfentanil BPND image in MNI 152 space (30 healthy controls) (MNI z -30).

11 Table 2.9. – Mean (±SD) [ C]Carfentanil BPND values in cortical regions using no mask or grey matter mask for ROI analysis in 32 healthy controls, including results from paired sample t-test. Grey Matter ROI No Mask p value Mask Cerebellum 0.74 (±0.24) 0.85 (±0.26) <0.001 DLPFc 0.84 (±0.17) 1.14 (±0.17) <0.001 Frontal operculum 1.26 (±0.17) 1.27 (±0.17) <0.001 MPFc 0.84 (±0.19) 1.11 (±0.18) <0.001 OFc 1.18 (±0.19) 1.38 (±0.20) <0.001 Parietal 0.62 (±0.12) 0.77 (±0.12) <0.001 Precentral G. 0.60 (±0.12) 0.74 (±0.11) <0.001 SMA 0.95 (±0.19) 1.07 (±0.18) <0.001 Temporal 0.99 (±0.14) 1.12 (±0.15) <0.001 Bonferroni corrected significance threshold p<0.006

107 2.12. Magnetic resonance imaging (MRI) procedures

Structural MRI and fMRI data were collected on a 3 Tesla Siemens Tim Trio systems running the syngo MR B17 software with a Siemens 32 channel receive-only phased-array head coil.

The imaging session was identical to that used in the ICCAM platform (McGonigle et al, 2017; Murphy et al, 2017; Nestor et al, 2017; Paterson et al, 2015) and consisted of localizer scans, main magnetic field mapping, one run of resting state (360 seconds); two runs of the MID task (432 seconds each), two runs of a go-nogo task (262 seconds each) and two runs of an evocative images task (392 seconds each). Following the tasks a block of structural scans were carried out including a high resolution structural scan for anatomical registration and radiological reporting, a proton density scan to provide a second contrast for radiological reporting, and a diffusion tensor imaging sequence (McGonigle et al, 2017).

Participants were in the scanner for approximately 80 minutes. All tasks were practiced outside the scanner immediately before the start of MRI scanning session.

2.12.1. MRI and fMRI data acquisition fMRI imaging was performed using the protocol developed for the ICCAM platform (McGonigle et al, 2017). The following is a summary of the protocol described in the McGonigle et al. paper. Multi-echo gradient echo echoplanar imaging was used with the following parameters: TR=2000ms, TE=13ms and 31ms, flip angle=80°, field of view=225mm, image matrix=64×64), in-plane resolution of 3.516×3.516mm and a slice thickness of 3.000 mm. The phase encoding direction was anterior to posterior. Echo spacing was 0.52ms. The second echo (TE=31ms) only was used for the fMRI analysis. 36 abutting oblique axial slices were collected for each volume in an ascending manner at a 30° angle to the anterior and posterior commissure line, resulting in the most superior 9 mm not being imaged in most participants (examples of this field of view can be seen in the whole-brain images in Chapter 5, Section 5.3.). Parallel imaging using GRAPPA with a 2 x

108 acceleration factor was performed. To allow for T1 saturation effects the first three volumes of each functional run were discarded and not included in any analysis (McGonigle et al, 2017).

2.12.2. MRI and fMRI data pre-processing

Structural and functional MRI data for all healthy control, alcohol dependent and gambling disorder participants listed in Table 2.1. were pre-processed by myself using an automated pipeline developed for the ICCAM platform by Dr John McGonigle (McGonigle et al, 2017). The following is a summary description of the pre-processing pipeline from the McGonigle et al. (2017) paper. The pipeline included the following toolboxes for pre-processing: AFNI (Analysis of Functional NeuroImages – version AFNI_2011_12_21_1014), FreeSurfer (version freesurfer-x86_64- unknown-linux-gnu-stable5-20130513), ANTs (Advanced Normalization Tools – version ANTs-1.9.v4-Linux), and FSL’s (FMRIB Software Library – version 5.0.6) FEAT (FMRI Expert Analysis Tool – version 6.00) run on CentOS 6.5 (version centos-release-6- 5.el6.centos.11.2.x86_64) (McGonigle et al, 2017).

Within the ICCAM pipeline T1 images were corrected for intensity non-uniformity (AFNI 3dUniformize) and then extracerebral tissues were removed (FreeSurfer recon-all pipeline). Whole-brain images were then non-linearly registered to the MNI 152 non-linear 6th generation symmetric average brain stereotaxic registration model in a 2mm isotropic voxel space using ANTs antsRegistration (McGonigle et al, 2017).

Echoplanar images (EPIs) were corrected for slice timing effects (using AFNI 3dTshift) before each volume was registered (using AFNI 3dvolreg) to the volume most similar to all others (in-house code developed by Dr John McGonigle). A summary of movement was recorded for the MID task as the speed of motion over the runs (mm/s). Residual extracerebral tissues were removed (using FSL BET) and then linear registration to the T1 image using Boundary Based Registration (BBR with FSL epi_reg). Transformations were combined to bring EPIs into the same standard stereotaxic space as the transformed T1 (using ANTs antsApplyTransforms). Finally data were smoothed with a three-dimensional Gaussian

109 kernel of full width at half maximum of 6.0mm (using AFNI 3dBlurInMask) (McGonigle et al, 2017).

2.13. ICCAM MID task

The ICCAM MID task design has been previously described by McGonigle et al. (2017) and in summary consisted of two runs of an event related design with blocks of several TRs in length (each run lasting 7 mins 12 sec with combined total of 14 min 24 sec). Each run of the task contained 18 neutral trials, 18 win trials, and 6 lose trials.

At the start of each trial, participants were shown cues on a screen indicating whether they were about to perform a win, lose or neutral trial. Following the cue, a blank black screen ‘anticipation period’ of variable duration (randomly selected as 2, 3 or 4 seconds) was shown prior to the target stimulus. Participants were required to respond with a button box to the target symbol whilst it remained present on the screen to ‘hit’ the target. If a participant successfully hit the target then there were three possible outcomes: winning £0.50 in a win trial, stopping the loss of £0.50 in a lose trial or neither in a neutral trial. The duration of time the target stimulus was present on the screen varied depending upon the performance of a participant during the task (McGonigle et al, 2017).

Initially for both runs of the task in the first win and neutral trials the target was present for 280 ms. If a participant ‘hit’ the target stimulus, the target duration dropped by 10 ms (down to a minimum of 150 ms). If a participant missed the target, the target duration increased by 10 ms (up to a maximum of 300 ms). The duration of the target symbols for each trial (win, neutral, lose) depended on the participant’s accuracy for the same trial type only (e.g. win trial accuracy only affected the duration of win trial target duration). This algorithm modifying target presentation duration times aimed to have an approximate accuracy for the win trials of 66% and approximate total winnings of £10 for the combined two task runs (out of a potential maximum of £18) (McGonigle et al, 2017).

110 0.5 seconds following the target symbol participants were informed of their performance (e.g. hit or miss) and their current winnings for the two trials up to that point for 2 seconds. The interval between the end of the outcome/winnings information and the onset of the next cue varied as 2.4, 3.4, or 4.4 seconds (McGonigle et al, 2017).

Cue ‘An2cipa2on’ Target Feedback 2, 3 or 4 s 150 - 300 ms

Win

Hit! Lose You have £4.50

Neutral

Figure 2.9. – ICCAM MID Task cues, target and feedback as seen by participants during the fMRI scan.

2.13.1. ICCAM MID task modelling

ICCAM MID task modelling was carried out by myself using an automated pipeline developed by Dr John McGonigle for analysing ICCAM fMRI task data using E-Prime (version 2.0.8.90), Microsoft Office Excel 2007 (version 12.0.4518.1014), in-house Python (version 2.7.6) scripts, and FSL. In summary, as detailed by McGonigle et al. (2017), the pipeline processed task response data from E-Prime into three-column-format text files compatible with FEAT. Pre- whitening was performed using FMRIB Improved Linear Modelling (FILM). Estimates of six motion parameters (three orthogonal directions and pitch, roll and yaw) were calculated during pre-processing (AFNI 3dvolreg) and were included in each model as confounding explanatory variables (McGonigle et al, 2017).

111

Convolution with a haemodynamic response function (HRF) was performed with a standard deviation of 3 seconds and mean lag of 6 seconds. All models had the same temporal filtering applied to them as in image data (McGonigle et al, 2017).

The ICCAM pipeline used nine EVs for modelling the MID task. Each of the three different general conditions: reward, neutral or loss, had three phases: anticipation, successful outcome and unsuccessful outcome. - ‘Anticipation’ was modelled as a block beginning at the cue onset and ending at the trial/target onset (blocks lasting approximately 3 seconds and 5 seconds). - ‘Outcome’ was modelled as an immediately abutting block beginning at the trial/target onset and lasting two seconds.

For each individual subject the two runs of the MID task were modelled separately in FEAT and then each contrast averaged across the two runs to produce the data used in ROI and ‘higher-level’ whole-brain analyses. The contrast of interest used for ROI and higher-level analyses is ‘win anticipation’ > ‘neutral anticipation’.

2.13.2. Quality control of pre-processed MRI data and MID task modelling

Prior to further analysis of the MID fMRI data a number of manual checks were carried out by myself to assess the suitability of individual participants’ data.

Firstly, non-linear transformations of structural MRI and EPI data to MNI 152 space were visually inspected to ensure that this was adequate for further analysis. Secondly, movement parameters were inspected for each participant (average movement per volume in mm/s) and plotted to assess for extreme movement or any outliers. No participant’s data were excluded due to poor non-linear registration or excessive movement during the task.

112 Finally, behavioural data were checked to ensure all participants responded to the tasks appropriately. One healthy control participant was excluded due to poor task performance (0% neutral trial accuracy).

All remaining analysis of the MID task, including ROI analyses and FSL FLAME modelling, were carried out by myself as detailed below.

2.14. ROIs for fMRI and combined PET and fMRI analyses

2.14.1. MID ‘functional’ ROIs

Bilateral ‘functional’ ROIs (fROIs) were generated for the fMRI data to ensure only voxels with significant win>neutral anticipation BOLD contrast (z>3.1 threshold) were included.

To generate the fROIs, bilateral structural 1mm NAcc, caudate, putamen, ventral pallidum ROIs were extracted from the CIC atlas. These structural 1mm ROIs were then resliced to 2mm using SPM12 coregister and reslice (4th degree B-Spline) and each 2mm structural ROI was visually inspected by overlaying on corresponding 1mm ROI to check for good fit.

To create fROIs from the structural ROIs, functional MID task data from 17 healthy controls who participated in a pilot study as part of the ICCAM platform and processed using the same method as above, were used. The mean thresholded whole-brain z-statistic map (z>3.1) for the win>neutral contrast this group, provided by Dr Louise Paterson (Figure 2.10.), was first transformed into a binarised mask (i.e. any voxels with significant contrast = 1, and no significant contrast = 0) and then this mask was combined with each of the four structural ROIs (NAcc, caudate, putamen, ventral pallidum) to create an ‘fROI’ which consists of only voxels contained in both the corresponding structural ROI and the mean z>3.1 z-statistic map mask (see Figure 2.11. for an example). SPM12 Imcalc was used to create the binarised masks and to create the fROIs.

113 ICCAM HC pilot mean win>neutral

z 5 z 0 Figure 2.10. – Mean MID win>neutral anticipation BOLD contrast in 17 healthy controls from the ICCAM pilot Z-30 Z3 study. Z23 Z48

z -5 z -10

Z Value 0 2 4 6 8

z 6 Figure 2.11. – Example of differences between CIC atlas ‘structural ROI’ and ‘functional ROI’ created using ICCAM MID pilot data: - Blue colour represents the fROI. - Red colour represents the remaining structural ROI that was not included in the fROI.

Whole-brain MID win>neutral anticipation BOLD contrast of parameter estimates (COPE) data were then extracted for each subject as percentage change in BOLD (%BOLD) signal for each fROI (see Figure 2.12.) using FSL FEAT query. These extracted MID fROI percentage signal

114 change data were used to compare win>neutral BOLD response between groups and for the combined PET and fMRI analyses.

z -10 y 7 x 12

1 2

3 4

Figure 2.12. – Functional ROIs used to extract MID win>neutral anticipation %BOLD signal change. (1. NAcc, 2. caudate, 3. putamen, 4. ventral pallidum)

11 2.14.2. Using ROI or fROI [ C]Carfentanil BPND values for combined PET and fMRI analyses

11 Whole-brain [ C]carfentanil BPND parametric image data were present in 1.5mm voxels. Both 2mm CIC ROIs and 2mm fROIs for the caudate, putamen, NAcc and ventral pallidum were resliced to 1.5mm using SPM12 coregister and reslice (4th degree B-Spline).

11 Mean [ C]Carfentanil BPND values for both structural ROIs and fROIs were extracted from whole-brain BPND parametric images in Matlab (2017a) by myself using a modified version of Matlab code written by Dr Jimmy (Chen-Chia) Lan. Correlational analyses (13 HC, 13 AD, 15

GD) were carried out to assess if there was any variation in extracted fROI BPND compared with structural ROI BPND. The fROI and structural ROI BPND values showed very good correlations across all 4 regions (Figure 2.13.). This close correlation suggests that using either structural atlas ROIs or function ROIs is unlikely to make a difference to the correlations between the PET and fMRI data.

115

Nucleus Accumbens Caudate

3.0 3.0 2.5 2.5 ND ND 2.0 2.0 1.5 1.5 1.0 1.0 Func8onal ROI BP Func8onal ROI BP Pearson’ R = 1.000 0.5 Pearson’ R = 0.999 0.5 0.0 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Atlas ROI BP Atlas ROI BP ND ND Putamen Ventral Pallidum

3.0 3.0 2.5 2.5 ND ND 2.0 2.0 1.5 1.5 1.0 1.0

Func8onal ROI BP 0.5 Pearson’ R = 0.997 Func8onal ROI BP 0.5 Pearson’ R = 0.990 0.0 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Atlas ROI BP Atlas ROI BP ND ND 11 Figure 2.13. – Association between structural Atlas ROI and functional ROI [ C]Carfentanil BPND values extracted from parametric maps including Pearson’s R values (including fitted y=x reference line).

The ROI and fROI BPND values extracted from parametric BPND images are a mean of BPND values from all 1.5mm voxels contained within the structural ROI or fROI mask. These voxel- wise BPND values are calculated by generating a TAC for each voxel and then modelling BPND values for each voxel using SRTM. An alternative method for obtaining ROI BPND values is to generate a single TAC for each distinct spatial ROI and then using SRTM to model a single BPND value for the entire ROI. This is the standard method for generating BPND values in MIAKAT and was used in the Colasanti et al. and Mick et al. [11C]Carfentanil PET dexamphetamine challenge publications (Colasanti et al, 2012; Mick et al, 2014, 2016).

It is therefore important to assess if using a different method to obtain ROI based

11 [ C]Carfentanil BPND values (e.g. whole ROI SRTM modelling versus extracting mean BPND

11 values from parametric maps) impacts on [ C]Carfentanil BPND values. Correlational analyses

116 were carried out examining the associations between whole ROI TAC modelled BPND and parametric map extracted BPND values. As can be seen in Figure 2.14. the BPND values extracted from parametric images are lower than the BPND from the non-parametric extracted

SRTM method and there is some variance between the BPND values between the two methods. When examining the ∆BPND values there is evidence of a much larger variance in

∆BPND values between the two methods (Figure 2.14.).

4.0 0.15 ND

3.5 ND 0.10 3.0 0.05 2.5 0.00 2.0 -0.05 1.5 Pearson’s R=0.82 NAcc Parametric ROI BP -0.10 Pearson’s R=0.56 NAcc Parametric ROI ∆BP 0 -0.15 0 1.5 2.0 2.5 3.0 3.5 4.0 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 NAcc Whole ROI TAC BP NAcc Whole ROI TAC ∆BP ND ND 11 Figure 2.14. – NAcc [ C]Carfentanil BPND and ∆BPND association between whole TAC ROI values generated in MIAKAT, and extracted values from BPND parametric images using the structural ROI mask including Pearson’s R values (including fitted x=y reference line).

Given that these BPND and ∆BPND data were from the spatially identical structural ROIs, this degree of variance is concerning. One possibility for this variance may be that single TAC ROI modelled BPND uses a larger brain volume to generate the TAC whilst parametric images generate an individual TAC for each voxel and may be more vulnerable to the effects of noise in the data. Given the possibility of higher noise in the parametric map ROI data and following a discussion with a PET methodologist (Dr Jim Myers) the decision was made to use whole

ROI TAC modelled BPND values, as outputted by MIAKAT, in the combined fMRI and PET analyses.

117 2.15. Whole-brain FMRIB's Local Analysis of Mixed Effects (FLAME) analyses

2.15.1. Comparing MID win>neutral anticipation BOLD contrast between groups

FMRIB's Local Analysis of Mixed Effects (FLAME) models were carried out in FSL to examine significant differences in the MID win>neutral anticipation BOLD contrast between groups. First a between-subject ANOVA model was carried out followed by post-hoc independent sample t-tests using similar models to those described in the FSL GLM wiki (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM).

2.15.2. FSL FLAME models examining associations between MID win>neutral

11 anticipation BOLD contrast and [ C]Carfentanil BPND/∆BPND ROI values

To examine if there were any associations between MID win>neutral anticipation BOLD

11 contrast and [ C]Carfentanil BPND and ∆BPND, demeaned ROI BPND or ∆BPND values were entered as an EV in the FSL FLAME model. Both positive and negative correlations were investigated. An example FSL FLAME model is shown in Figure 2.15. examining correlations between ventral pallidum BPND and MID win>neutral anticipation BOLD contrast in gambling disorder participants.

118 Figure 2.15. – Example of FSL FLAME model examining correlations between PET and fMRI data: This example examines positive and

11 negative correlations between ventral pallidum [ C]carfentanil BPND and MID win>neutral anticipation BOLD contrast in 15 gambling disorder participants.

11 2.15.3. Interactions between effects of status and [ C]Carfentanil BPND/∆BPND on MID win>neutral anticipation BOLD contrast

To test if there is a significant interaction between the effects of status (i.e. alcohol dependent versus healthy control, or alcohol dependent versus gambling disorder) and [11C]Carfentanil

BPND/∆BPND on MID win>neutral anticipation BOLD contrast, a FSL FLAME model similar to the to the ‘two groups with continuous covariate interaction’ model from the FSL GLM wiki (fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#Two_Groups_with_continuous_covariate_interaction) was used. This model examined if the correlation between MID win>neutral anticipation

11 BOLD contrast and [ C]Carfentanil BPND/∆BPND was significantly different between two groups. The FSL FLAME model examining the interaction between the effects of status

11 (healthy control or alcohol dependent) and putamen [ C]carfentanil BPND on MID win>neutral anticipation BOLD contrast in is shown as an example in Figure 2.16.

119 Figure 2.16. – Example FSL FLAME model examining the interaction between the effects of status and putamen

11 [ C]carfentanil BPND on MID win>neutral anticipation BOLD contrast in 13 healthy controls and 13 alcohol dependent participants.

2.15.4. The effect of OPRM1 genotype on MID win>neutral anticipation BOLD contrast

To examine if there were any significant whole-brain differences in MID win>neutral anticipation BOLD contrast between OPRM G-allele carriers (G:A or G:G) and A-allele homozygous (A:A) individuals similar FSL FLAME independent sample t-tests to those described in FSL GLM wiki were carried out.

2.15.5. Interactions between effects of OPRM1 genotype and [11C]Carfentanil

BPND/∆BPND on MID win>neural anticipation BOLD contrast

To test if there is a significant interaction between the effects of OPRM1 genotype and

11 [ C]Carfentanil BPND/∆BPND on MID BOLD responses the ‘Two Groups with continuous covariate interaction’ FLAME model from the FSL GLM wiki was used. See Figure 2.17. for an example FLAME model.

120 Figure 2.17. – Example FSL FLAME model examining the interaction between the effects of OPRM1 genotype and

11 putamen [ C]carfentanil BPND on MID win>neutral anticipation BOLD contrast in 13 healthy controls.

2.16. Power calculations

The power calculation for comparing MOR availability between healthy controls and alcohol dependent participants was based on previous PET studies using [11C]carfentanil and [11C]diprenorphine (Heinz et al, 2005; Weerts et al, 2011; Williams et al, 2009). These

11 reported 18-26% higher [ C]carfentanil BPND in the NAcc in alcohol dependent participants compared with healthy controls, and 8% higher global [11C]diprenorphine volume of distribution (VD) in alcohol dependent participants. Mick et al. (Mick et al, 2016) reported

11 mean NAcc [ C]carfentanil BPND of 2.76 (SD±0.40) in healthy controls. Assuming that alcohol

11 dependent participants will have a 15% higher [ C]carfentanil BPND than controls a calculated sample size of 15 in each group is required to reject the null hypothesis with 80% power and alpha=0.05.

The power calculation to compare endogenous opioid release in alcohol dependent

11 participants was based on [ C]carfentanil ∆BPND results in gambling disorder and healthy

11 controls from Mick et al. which showed significant differences in [ C]carfentanil ∆BPND with 15 participants in each group (Mick et al, 2016). Regions which showed significant differences

11 in [ C]carfentanil ∆BPND between gambling disorder and healthy controls were the insula, frontal lobe, anterior cingulate, putamen and cerebellum. Sample size calculations (80%

11 power and alpha=0.05) estimating similar differences in [ C]carfentanil ∆BPND between

121 alcohol dependent participants and healthy controls in these regions give required numbers required in each group ranging from 10 to 26 to reject the null hypothesis dependent on the ROI.

11 As stated in Section 2.2. baseline (or pre-dexamphetamine) [ C]carfentanil BPND data were

11 available for 32 healthy controls and post-dexamphetamine [ C]carfentanil BPND data were available for 20 healthy controls. Therefore our healthy control participants numbers are

11 within the required sample sizes for adequate power in the [ C]carfentanil BPND and ∆BPND group comparison analyses.

Based on the power calculations, the decision was made to scan up to 15 alcohol dependent participants. Unfortunately, due to changes in the clinical services from which alcohol dependent participants were being recruited, it took longer than planned to recruit 15 alcohol dependent participants. The consequence of this was that only 13 alcohol dependent participants were recruited prior to the end of the grant that was funding the study.

Due to the failure to meet the recruitment target of 15 alcohol dependent participants, our analyses were underpowered to detect the hypothesised group difference of 15% in baseline

11 [ C]carfentanil BPND. Our analyses were also underpowered to detect differences in

11 [ C]carfentanil ∆BPND between our alcohol dependent and healthy control groups in some ROIs.

Furthermore, the clinical studies providing the data for this thesis were not designed to be

11 powered for correlational analyses examining the associations between [ C]carfentanil BPND or ∆BPND and demographic or clinical variables and these should be considered as secondary analyses. Similarly, the studies were not designed to be powered for the combined PET/fMRI analyses, and these analyses should also be considered secondary and exploratory.

Underpowered analyses are prone to higher rates of both type 1 (false positive) and type 2 (false negative) errors (Button et al, 2013). Therefore, the results in this thesis were corrected for multiple comparisons, as discussed in more detail in Section 2.9.3.. The results

122 surviving multiple comparison correction will be presented as the primary and most reliable findings of this thesis.

123

124 CHAPTER 3: INVESTIGATING MOR AVAILABILITY IN ABSTINENT ALCOHOL DEPENDENT PARTICIPANTS

3.1. Introduction

3.1.1. Aims

This chapter examines MOR availability in healthy controls and alcohol dependent participants using [11C]carfentanil PET. The aims of this chapter are:

11 1. To examine if there are differences in [ C]carfentanil BPND (i.e. MOR availability) in alcohol dependent participants compared with healthy controls. 2. To examine if factors relating to alcohol dependence, for example duration of abstinence, severity of dependence or heavy alcohol use, are associated with

11 differences in [ C]carfentanil BPND (i.e. MOR availability).

3.1.2. Introduction to MOR availability results chapter

As discussed in Chapter 1, Section 1.5.2., there is evidence of higher MOR availability in alcohol dependent participants in early abstinence (Heinz et al, 2005; Hermann et al, 2017; Weerts et al, 2011; Williams et al, 2009) which has been associated with higher craving (Heinz et al, 2005; Williams et al, 2009), and may play an important role in treatment responses to naltrexone (Hermann et al, 2017). This chapter will examine if there is higher MOR availability in alcohol dependent individuals who have longer durations of abstinence than the alcohol dependent participants in the previously published studies. It will also examine if MOR availability is associated with clinical variables associated with alcohol use including craving, lifetime alcohol use, severity of dependence, risk of relapse and duration of abstinence. This may help to identify which aspects of alcohol dependence are related to changes in MOR availability and to help better understand the mechanisms of these changes. High alcohol craving in early abstinence is associated with higher MOR availability in the ventral striatum

125 (NAcc) (Heinz et al, 2005; Williams et al, 2009), therefore the NAcc will be the a priori ROI to examine the associations between alcohol related clinical variables and MOR availability.

Similarly to the alcohol use related variables above, the associations between other clinical variables, including impulsivity, depression and anxiety symptoms, and MOR availability will also be examined. Anxiety and depression, are commonly associated with alcohol dependence and other addictions (Lingford-Hughes et al, 2012), and differences in MOR availability have been shown in major depression (Kennedy et al, 2006; Zubieta et al, 1999). In gambling disorder higher UPPS-P Negative Urgency scores are associated with higher caudate MOR availability (Mick et al, 2016). Similarly to the alcohol related variables, associations between anxiety and depressive symptoms and MOR availability will be examined a priori in the NAcc ROI. Associations between MOR availability and UPPS-P Negative Urgency will be examined a priori in the caudate ROI.

In addition to the clinical variables related to alcohol dependence, differences in MOR availability associated with current smoking will also be examined to assess if smoking may be a confounding factor in the comparison of MOR availability between healthy controls and alcohol dependent participants. The OPRM1 A118G polymorphism has been associated with

11 lower [ C]carfentanil BPND (Domino et al, 2015; Nuechterlein et al, 2016; Peciña et al, 2015b; Ray et al, 2011; Weerts et al, 2013) and therefore it will also be examined if this polymorphism may be confounding the comparison of MOR availability between healthy controls and alcohol dependent participants.

3.1.3. Hypotheses:

1. There is higher MOR availability in alcohol dependent participants compared with healthy controls. 2. Higher NAcc MOR availability is associated with alcohol dependence related clinical variables including craving, lifetime alcohol use, severity of dependence, risk of relapse and duration of abstinence in alcohol dependent participants.

126 3. There are positive correlations between caudate MOR availability and UPPS-P Negative Urgency scores in alcohol dependent participants.

3.2. Methods

Baseline [11C]carfentanil PET data were available for 32 healthy controls and 13 alcohol dependent participants. More details of the subject groups, data collection and methods used to process the [11C]carfentanil PET data are available in the Methods Chapter (Chapter 2).

3.3. Results

3.3.1. Demographics

Available demographic data for healthy controls and alcohol dependent participants are presented in Table 3.1.. Healthy controls were significantly younger with lower Spielberger Trait Anxiety, UPPS-P Positive and Negative Urgency scores. Alcohol dependent participants were more likely to be smokers and have a higher dependence to nicotine if they smoked (FTND scores). Alcohol dependent participants also had significantly higher cold carfentanil mass injected in their PET scans.

127 Table 3.1. – Demographic measures (mean ±SD) compared between healthy controls and alcohol dependent participants, including p values from independent-sample t-test and Mann Whitney U test (BDI and Alcohol Abstinence) comparing between groups. Data shown for 32 healthy controls and 13 alcohol dependent participants unless otherwise indicated. HC AD Variable p value (total 32) (total 13) Age 39.6 (±10.2) 46.6 (±7.3) 0.029 BMI 25.0 (±4.0) 26.7 (±3.7) 0.220 (20 HC) Alcohol abstinence (days) 604.6 8.4 (±11.4) <0.001 (14 HC) (±866.5) Current Smokers 5 (16%) 7 (54%) Cigarettes per day in smokers 8 (±4.5) 10.6 (±7.7) 0.520 (5 HC, 7 AD) Fagerström Test for Nicotine Dependence scores in current smokers 2.2 (±2.3) 3.7 (±3.1) 0.024 (5 HC, 7 AD) Pack years in current and ex-smokers 7.8 (±6.9) 23.6 (±14.5) 0.377 (6 HC, 11 AD) STAI 0.002 30.0 (±6.5) 37.2 (±5.9) (29 HC) BDI 1.5 (±3.1) 3.3 (±3.6) 0.072 (30 HC) BIS total score 51.3 (±7.9) 49.2 (±4.8) 0.391 (29 HC) Negative Urgency 22.4 (±6.2) 27.4 (±4.1) 0.017 Lack of 22.3 (±4.7) 22.8 (±3.5) 0.714 premeditation UPPS-P Lack of (20 HC, 12 AD) 18.4 (±4.1) 18.5 (±3.8) 0.918 perseverance Sensation Seeking 31.4 (±8.2) 33.1 (±6.0) 0.542 Positive Urgency 23.0 (±6.1) 30.8 (±4.9) 0.001 Injected cold carfentanil mass (baseline or 1.3 (±0.5) 1.7 (±0.5) 0.011 pre-dexamphetamine scan) (μg)

11 3.3.2. Comparison of baseline [ C]carfentanil BPND between healthy controls and alcohol dependent participants

11 A mixed model ANOVA examining differences in [ C]carfentanil BPND between healthy controls and alcohol dependent participants showed a significant within-subject effect of ROI and between-subject effect of Status but no significant ROI x Status interaction (Table 3.2.).

128 Table 3.2. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between-

11 subject effect of Status (alcohol dependent or healthy control) on [ C]Carfentanil BPND in 32 healthy controls and 13 alcohol dependent participants. Effects F-ratio (effect df, error df) p value ROI 397.6 (5.2, 221.7) <0.001 Within-subject factors ROI x Status 1.8 (5.2, 221.7) 0.105 Between-subject factors Status 4.8 (1, 43) 0.034

Post-hoc independent sample t-tests showed a trend (uncorrected p<0.05) towards lower

11 [ C]carfentanil BPND in alcohol dependent participants in a number of regions including ventral pallidum, amygdala and DLPFc. However, none of these t-tests survived Bonferroni correction for multiple comparisons (corrected significance threshold p<0.0024) or P-plot Hochberg correction (Table 3.3., Figure 3.1.).

11 Table 3.3. – Mean (±SD) [ C]carfentanil BPND in 32 healthy controls and 13 alcohol dependent participants in 21 ROIs, including p values from independent sample t-tests comparing BPND between groups (no significant tests following Bonferroni corrected significance threshold p<0.0024 or P-plot Hochberg correction).

11 Mean (±SD) [ C]carfentanil BPND ROI Healthy Alcohol p value Controls dependent NAcc 2.79 (±0.32) 2.64 (±0.31) 0.148 Amygdala 1.72 (±0.21) 1.57 (±0.19) 0.029 Anterior cingulate 1.47 (±0.20) 1.37 (±0.11) 0.046 Caudate 1.39 (±0.29) 1.30 (±0.26) 0.330 Cerebellum 0.85 (±0.26) 0.79 (±0.17) 0.420 DLPFC 1.14 (±0.17) 1.04 (±0.10) 0.019 Frontal operculum 1.27 (±0.17) 1.23 (±0.10) 0.325 Globus pallidus 1.39 (±0.24) 1.34 (±0.20) 0.479 Hippocampus 0.73 (±0.12) 0.64 (±0.12) 0.048 Hypothalamus 1.86 (±0.34) 1.57 (±0.41) 0.018 Insula 1.48 (±0.18) 1.43 (±0.13) 0.406 MPFc 1.11 (±0.18) 1.04 (±0.14) 0.217 Orbitofrontal 1.38 (±0.20) 1.31 (±0.12) 0.179 Parietal lobe 0.77 (±0.12) 0.71 (±0.07) 0.068 Posterior 0.84 (±0.13) 0.75 (±0.10) 0.038 cingulate Precentral G. 0.74 (±0.11) 0.70 (±0.07) 0.199 Putamen 1.85 (±0.23) 1.83 (±0.16) 0.803 SMA 1.07 (±0.18) 1.00 (±0.17) 0.249 Temporal lobe 1.12 (±0.15) 1.07 (±0.08) 0.201 Thalamus 1.68 (±0.19) 1.58 (±0.17) 0.105 Ventral pallidum 2.65 (±0.34) 2.36 (±0.26) 0.008

129

Ventral pallidum Thalamus

Temporal lobe Healthy Control SMA Alcohol Dependent Putamen Precentral G. Posterior cingulate Parietal lobe Orbitofrontal MPFC Insula Hypothalamus Hippocampus Globus pallidus Frontal operculum DLPFC Cerebellum Caudate Anterior cingulate Amygdala NAcc 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 [¹¹C]carfentanil BP ND 11 Figure 3.1. – Mean (±SD) [ C]carfentanil BPND in 32 healthy controls and 13 alcohol dependent participants in 21 high-BPND ROIs.

3.3.3. Associations between MOR availability and duration of abstinence from alcohol

11 To examine the effect of abstinence on [ C]Carfentanil BPND in alcohol dependent

11 participants, a correlation analysis was carried out between NAcc [ C]Carfentanil BPND and days of abstinence, using Spearman’s Rank coefficient. This did not show a significant correlation (rho=0.475, p=0.10). Further exploratory analysis using the remaining 20 ROIs also

130 11 showed no significant correlations between [ C]Carfentanil BPND and days of abstinence (Bonferroni corrected significance threshold p<0.0024).

3.3.4. Associations between MOR availability and lifetime high risk alcohol exposure

Spearman’s rank coefficient was used to examine the association between lifetime heavy alcohol exposure (defined as total weeks consuming mean >60g alcohol/day) and NAcc

11 [ C]carfentanil BPND. There was no significant correlation (rho=0.043, p=0.889) and further exploratory analysis including the remaining 20 ROIs also showed no significant correlations

11 between [ C]Carfentanil BPND and lifetime heavy alcohol exposure (Bonferroni corrected significance threshold p<0.0024).

3.3.5. Associations between MOR availability and SADQ scores

The association between severity of alcohol dependence, as measured with SADQ scores, and

11 NAcc [ C]carfentanil BPND was examined using Pearson’s correlation coefficient. There was no significant correlation (R=0.203, p=0.505). Further exploratory analyses including the remaining 20 ROIs also showed no significant correlations (Bonferroni corrected significance threshold p<0.0024).

3.3.6. Associations between MOR availability and TRQ scores

The association between risk of relapse, as measured with TRQ scores, and NAcc

11 [ C]carfentanil BPND was examined using Pearson’s correlation coefficient. There was no

11 significant correlation between TRQ scores and [ C]carfentanil BPND (R=-0.082, p=0.791), and further exploratory analysis showed no significant correlations when including the remaining 20 ROIs (Bonferroni corrected significance threshold p<0.0024).

131 3.3.7. Alcohol Urge Questionnaire scores

None of our alcohol dependent participants reported any craving for alcohol, measured with the AUQ, at screening or at any other point during the study including before and after the dexamphetamine challenge. For this reason it was not possible to correlate craving scores

11 with [ C]carfentanil BPND.

3.3.8. Associations between MOR availability and UPPS-P scores

Pearson’s correlation coefficient analyses were carried out to examine association between

11 UPPS-P Negative Urgency and caudate [ C]carfentanil BPND in alcohol dependent participants. This did not show any significant correlations between UPPS-P Negative Urgency

11 and caudate [ C]carfentanil BPND in alcohol dependent participants (R=-0.031, p=0.923). Further exploratory analysis in alcohol dependent participants in the remaining 20 ROIs also did not show any significant correlations between UPPS-P Negative Urgency and

11 [ C]carfentanil BPND (all uncorrected p>0.05).

Due to the significantly higher UPPS-P Positive Urgency scores in alcohol dependent participants compared with healthy controls (see Table 3.1.), associations between UPPS-P

11 Positive Urgency scores and caudate [ C]carfentanil BPND was also explored using Pearson’s correlation coefficient. There was no significant correlation (R=0.072, p=0.823), and further exploratory analysis including the remaining 20 ROIs also did not show any significant

11 correlations between UPPS-P Negative Urgency and [ C]carfentanil BPND (all uncorrected p>0.05).

3.3.9. Associations between MOR availability, BDI and STAI and SSAI scores

Pearson’s correlation coefficient and Spearman’s rank coefficient analyses were carried out

11 to examine the associations between NAcc [ C]Carfentanil BPND and STAI/SSAI and BDI scores in healthy controls and alcohol dependent participants separately. There were no significant

132 11 correlations between NAcc [ C]Carfentanil BPND and STAI (HC: R=0.329, p=0.081, AD: R=0.181, p=0.554) or BDI (HC: rho=-0.091, p=0.633, AD: rho=-0.161, p=0.599) scores and further exploratory analyses including the remaining 20 ROIs also showed no significant correlations (Bonferroni corrected significance threshold p<0.0024).

There were no significant correlations in either healthy controls or alcohol dependent

11 participants between NAcc [ C]Carfentanil BPND and SSAI scores completed on the screening visit (HC: R=0.226, p=0.457, AD: R=0.074, p=0.659) or on the PET visit prior to the pre- dexamphetamine [11C]Carfentanil PET scan (HC: R=0.238, p=0.507, AD: R=0.195, p=0.301). Exploratory analyses did not show any significant correlations between SSAI scores and

11 [ C]Carfentanil BPND in any of the remaining 20 ROIs (Bonferroni corrected significance threshold p<0.0024).

3.3.10. Examining the potential confounding effect of smoking and nicotine

11 dependence on [ C]carfentanil BPND

11 Mixed model ANOVAs showed no effect of current smoking status on [ C]Carfentanil BPND in healthy controls or alcohol dependent participants (Tables 3.4. and 3.5.).

Table 3.4. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between-

11 subject effect of Smoking (current smoker or non-smoker) on [ C]Carfentanil BPND in 32 healthy controls. Effect F-ratio (effect df, error df) p value ROI 198.3 (5.2, 156.4) <0.001 Within-subject factors ROI x Smoking 1.1 (5.2, 156.4) 0.378 Between-subject factors Smoking 0.0 (1, 30) 0.837

133 Table 3.5. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between-

11 subject effect of Smoking (current smoker or non-smoker) on [ C]Carfentanil BPND in 13 alcohol dependent participants. Effect F-ratio (effect df, error df) p value ROI 130.4 (3.5, 38.9) <0.001 Within-subject factors ROI x Smoking 0.8 (3.5, 38.9) 0.549 Between-subject factors Smoking 0.0 (1, 11) 0.988

A further mixed model ANOVA combining healthy controls and alcohol dependent participants showed no significant Smoking x Status interaction (Table 3.6., Figure 3.2.)

Table 3.6. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between- subject effects of Smoking (current smoker or non-smoker) and Status (healthy control or alcohol

11 dependent) on [ C]Carfentanil BPND in 32 healthy controls and 13 alcohol dependent participants. Effect F-ratio (effect df, error df) p value ROI 316.8 (5.1, 211.0) <0.001 ROI x Smoking 0.3 (5.1, 211.0) 0.934 Within-subject factors ROI x Status 1.1 (5.1, 211.0) 0.365 ROI x Smoking 1.5 (5.1, 211.0) 0.189 x Status Smoking 0.0 (1, 41) 0.876 Status 3.2 (1, 41) 0.079 Between-subject factors Smoking x 0.0 (1, 41) 0.889 Status

134 Ventral pallidum

Thalamus

Putamen

Healthy Control Orbitofrontal Non-smokers Healthy Control Insula Smokers Alcohol Dependent Hypothalamus Non-smokers Alcohol Dependent Smokers Cerebellum

Caudate

Anterior cingulate

Amygdala

NAcc

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 [¹¹C]carfentanil BP ND 11 Figure 3.2. – Mean (±SD) [ C]Carfentanil BPND in 5 current smoking and 25 current non-smoking healthy controls and 7 current smoking and 6 current non-smoking alcohol dependent participants across 10 ROIs.

Spearman’s rank coefficient analyses carried out to test for correlations between FTND scores

11 and NAcc [ C]carfentanil BPND showed no significant correlation in either healthy control smokers (rho=0.620, p=0.265) or alcohol dependent smokers (rho=-0.490, p=0.265). Further exploratory analysis including the remaining 20 ROIs also showed no significant correlations

11 between FTND scores and [ C]carfentanil BPND (all uncorrected p>0.05).

135 3.3.11. Examining the associations between age and MOR availability

11 Mixed model ANOVAs were used to examine the effect of age on [ C]Carfentanil BPND in healthy controls and alcohol dependent participants separately and showed a significant ROI x Age interaction in healthy controls but not in alcohol dependent participants (Tables 3.7. and 3.8.).

Table 3.7. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between-

11 subject effect of age on [ C]Carfentanil BPND in 32 healthy controls. Effect F-ratio (effect df, error df) p value ROI 35.1 (5.4, 162.5) <0.001 Within-subject factors ROI x Age 4.6 (5.4, 162.5) <0.001 Between-subject factors Age 1.1 (1, 30) 0.305

Table 3.8. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between-

11 subject effect of Age on [ C]Carfentanil BPND in 13 alcohol dependent participants. Effect F-ratio (effect df, error df) p value Within-subject factors ROI 7.5 (4.1, 45.2) <0.001 ROI x Age 2.5 (4.1, 45.2) 0.051 Between-subject factors Age 0.6 (1, 11) 0.450

The significant ROI x Age interaction in healthy volunteers was further examined using an exploratory Pearson’s correlation coefficient examining the correlation between

11 [ C]Carfentanil BPND in all 21 ROIs and age. Of the 21 ROIs examined there were correlations with p<0.05 in 7 regions (6 positive correlations, 1 negative correlation), of which only the

11 correlation between Putamen [ C]Carfentanil BPND and age survives P-plot Hochberg correction (Table 3.9., Figure 3.3.).

136 Table 3.9. – Selected results from Pearson’s correlation coefficient examining correlations between age and

11 [ C]Carfentanil BPND in 21 ROIs in 32 healthy controls. Region Pearson’s R p value Anterior cingulate 0.397 0.024 Frontal Operculum 0.464 0.008 Hypothalamus -0.483 0.005 Insula 0.400 0.023 OFc 0.381 0.031 Putamen 0.514 0.003* Temporal Lobe 0.464 0.008 (Bonferroni corrected significance threshold p<0.0024, *significant test following P-plot Hochberg correction)

Putamen (R=0.514, p=0.003) 2.4 11 ND Figure 3.3. – Age and putamen [ C]carfentanil 2.1 BPND in 32 healthy control participants with 1.8 results from Pearson’s correlation coefficient.

C]carfentanil BP 1.5 ¹¹ 1.2 Putamen [ 0 0 10 20 30 40 50 60 70 Age (years)

3.3.12. Examining the potential confounding effect of the OPRM1 A118G polymorphism on MOR availability

In the healthy control participants OPRM1 A118G polymorphism data were available in 20 participants, of which 5 were G-allele carriers (G:A or G:G). OPRM1 A118G polymorphism data were available for all 13 alcohol dependent participants of which 4 were G-allele carriers (G:A or G:G).

A mixed model ANOVA examining the interaction between OPRM1 A118G polymorphism and

11 alcohol dependence on [ C]Carfentanil BPND showed a significant between-subject effect of Genotype, but no significant Status x Genotype interactions (Table 3.10.).

137 Table 3.10. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between- subject effects of Status (healthy control or alcohol dependent) and Genotype (G-allele carrier G:G/G:A or

11 A-allele homozygous A:A) on [ C]Carfentanil BPND in 20 healthy controls (5 G-allele carriers) and 13 alcohol dependent participants (4 G-allele carriers). Effect F-ratio (effect df, error df) p value ROI 261.4 (4.6, 133.7) <0.001 ROI x Status 1.4 (4.6, 133.7) 0.219 Within-subject factors ROI x Genotype 1.7 (4.6, 133.7) 0.136 ROI x Status x 0.4 (4.6, 133.7) 0.868 Genotype Status 5.6 (1, 29) 0.024 Genotype 6.8 (1, 29) 0.014 Between-subject factors Status x 0.0 (1, 29) 0.863 Genotype

Given the lack of significant Status x Genotype interactions, but a significant effect of Status

11 and Genotype in the mixed model ANOVA analysis, differences in [ C]Carfentanil BPND between G-allele carriers and A-allele homozygous individuals were examined separately in healthy controls and alcohol dependent participants using independent sample t-tests. In

11 healthy controls there was a trend towards lower [ C]Carfentanil BPND in G-allele carriers in all regions (uncorrected p<0.05 in amygdala, hippocampus and ventral pallidum), but these differences were only significant in the ventral pallidum following P-plot Hochberg correction for multiple comparisons. In alcohol dependent participants a similar trend of lower

11 [ C]Carfentanil BPND in G-allele carriers was present (p<0.05 in NAcc and thalamus), with the exception of the posterior cingulate. There were no significant differences in [11C]Carfentanil

BPND between alcohol dependent G-allele carriers and A-allele homozygous individuals following correction for multiple comparisons.

11 Further exploratory analyses were carried out examining differences in [ C]Carfentanil BPND between G-allele carriers and A-allele homozygous individuals in a combined healthy control and alcohol dependent participant group using independent sample t-tests. The trend of

11 lower [ C]Carfentanil BPND in G-allele carriers was present in all regions (p<0.05 in NAcc, anterior cingulate, hypothalamus, insular, thalamus and ventral pallidum), with only differences in the thalamus remaining significant following P-plot Hochberg correction for multiple comparisons (Figure 3.4.).

138 * Ventral pallidum ** Thalamus Temporal lobe SMA Putamen Precentral G. Posterior cingulate A-Allele Homozygous (A:A) Parietal lobe G-Allele Carrier (G:A or G:G) Orbitofrontal MPFC Insula * * Hypothalamus Hippocampus Globus pallidus Frontal operculum DLPFC Cerebellum Caudate Anterior cingulate * Amygdala * NAcc 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 [¹¹C]carfentanil BP ND 11 Figure 3.4. – Mean (±SD) [ C]carfentanil BPND in 24 OPRM1 A:A (A-allele homozygous) and 9 OPRM1 G- allele carriers (G:A or G:G) across 21 ROIs in combined healthy control and alcohol dependent cohort. Including results for independent sample t-test (* uncorrected p<0.05, ** significant test following P-plot Hochberg correction).

3.3.13. Cold carfentanil mass on effect on MOR availability

Cold carfentanil mass was significantly higher in healthy controls compared with alcohol dependent participants (see Table 3.1.). There were also significant differences in cold carfentanil mass compared between the different groups of healthy controls, with the lowest cold mass in the earliest Colasanti et al. (2012) study (see Table 3.11.) and highest in the alcohol dependent study participants.

139

Table 3.11. – Mean (±SD) injected cold carfentanil mass in three different healthy control participant groups and alcohol dependent participants. Mean (±SD) injected cold Participant Group carfentanil mass (μg) Healthy controls from Colasanti et al. 0.82 (±0.32) study (n=12) Healthy controls from Mick et al. 1.27 (±0.11) * studies (n=9) Healthy controls from alcohol 1.69 (±0.44) *, † dependent and acetate studies (n=11) Alcohol dependent participants (n=13) 1.69 (±0.47) *, † * significant independent t-test compared with Colasanti et al. healthy controls † significant independent t-test compared with Mick et al. healthy controls

11 To examine whether the trend of lower [ C]carfentanil BPND in alcohol dependent participants may be due to higher injected cold carfentanil mass compared with healthy

11 controls, associations between [ C]carfentanil BPND in all 21 ROIs and injected carfentanil mass were examined in 31 healthy controls and in 13 alcohol dependent participants.

In healthy controls there was a positive correlation between injected cold carfentanil mass

11 (μg) and [ C]carfentanil BPND in the amygdala only (p=0.0023, Bonferroni corrected significance threshold p<0.0024, Figure 3.5.). In alcohol dependent participants there were no significant correlations between injected cold carfentanil mass (μg) and [11C]carfentanil

BPND in any ROI.

Amygdala Figure 3.5. – Cold carfentanil mass (R=0.526, p=0.002) 2.2 (μg) and Amygdala [11C]carfentanil

BPND in 31 healthy controls with results ND 1.9

1.6 from Pearson’s correlation coefficient.

1.3 C]carfentanil BP

¹¹ 1.0 [

0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Cold carfentanil mass (μg)

140 3.4. Discussion

In this chapter the results did not show significantly higher MOR availability in abstinent alcohol dependent participants compared with healthy controls. There were also no significant associations between MOR availability in abstinent alcohol dependent participants and any of the examined clinical variables associated with alcohol dependence.

11 3.4.1. Comparisons of [ C]carfentanil BPND between healthy controls and alcohol dependent participants

It was hypothesised that there would be higher MOR availability in alcohol dependent participants in keeping with two published studies using [11C]carfentanil (Heinz et al, 2005; Weerts et al, 2011) and one study using non-specific opioid receptor ligand [11C]diprenorphine (Williams et al, 2009). However, MOR availability was not higher in our population of alcohol dependent participants. There were some regions, including the ventral pallidum, amygdala and DLPFc, which showed a trend towards lower MOR availability although this did not survive correction for multiple comparisons.

One potential reason for the lack of higher MOR availability in our alcohol dependent participants compared with other published studies may be a difference in the duration of abstinence from alcohol. Our participants were abstinent from alcohol for a minimum of 4 weeks, with most abstinent for longer durations than this (months to years). In contrast, other published studies’ participants were scanned shortly following stopping alcohol, with a range from 5 to 30 days of abstinence from alcohol (Heinz et al, 2005; Hermann et al, 2017; Weerts et al, 2011; Williams et al, 2009).

There is post-mortem evidence of lower MOR receptor concentrations, as measured with MOR agonist [3H]DAMGO autoradiography, in the caudate and ventral striatum of alcohol dependent individuals (Hermann et al, 2017). This is in contrast with the higher MOR availability described in early abstinence using [11C]carfentanil and [11C]diprenorphine(Heinz

141 et al, 2005; Hermann et al, 2017; Weerts et al, 2011; Williams et al, 2009). Hermann et al. (2017) proposed that continuous alcohol use, which leads to chronically raised endogenous opioid concentrations, may cause reductions in absolute MOR numbers as a compensatory mechanism (see Figure 3.6.). These raised endogenous opioid concentrations from heavy repeated alcohol administration may also lead to compensatory reductions in endogenous opioid tone, possibly through homeostatic inhibition of POMC activity (Pennock and Hentges, 2011). Once alcohol use is stopped, for example during early abstinence following a detoxification, higher MOR availability may be observed as although there are lower MOR concentrations, there is also low endogenous opioid tone leading to a relative reduction in MOR receptors occupied by endogenous opioids.

Figure 3.6. – Proposed hypothesis of opioid homeostasis in relation to alcohol use in alcohol dependence (adapted from Hermann et al. 2017) showing higher MOR availability in ‘early abstinence’. The ‘late abstinence’ period represents our alcohol dependent participants without higher MOR availability.

Hermann et al. (2017) also proposed that with prolonged abstinence there may be a recovery of endogenous opioid signalling. This may lead to a ‘normalisation’ of MOR receptor concentration or the relative availability of MOR, or both. This ‘normalisation’ could be a reason for the lack of higher MOR availability in longer term abstinent alcohol dependent

142 individuals, as shown in our cohort. However, whilst there is evidence that high MOR availability in cocaine dependence ‘normalises’ during the first 12 weeks of abstinence (Gorelick et al, 2005), MOR availability appears to be more stable in alcohol dependence during a similar time period in early abstinence (Heinz et al, 2005; Williams et al, 2009).

We do not observe any associations between MOR availability and duration of abstinence in our alcohol dependent participants that might confirm a ‘normalisation’ of MOR availability with abstinence. Furthermore, as will be demonstrated in Chapter 4, endogenous opioid tone is blunted in long term abstinence from alcohol, and therefore the proposed recovery of opioid tone by Hermann et al. (Figure 3.6.) may not occur.

Another possibility for the lack of higher MOR availability in our alcohol dependent participants in contrast with other studies could be related to a higher relapse risk in individuals with higher MOR availability. Higher MOR availability is associated with increased relapse risk in cocaine dependence (Ghitza et al, 2010; Gorelick et al, 2008), although this association between high MOR availability and risk of relapse has not been similarly examined in alcohol dependence. There is, however, evidence in alcohol dependence that higher craving is associated with higher MOR and non-specific opioid receptor availability during early abstinence (Heinz et al, 2005; Williams et al, 2009) which may result in a higher risk of relapse. Alcohol dependent individuals, such as those in our study, who have managed to attain long durations of stable abstinence may have had lower MOR availability in early abstinence and were therefore more likely to remain abstinent for longer periods of time. This may introduce a recruitment bias towards alcohol dependent individuals without higher MOR availability in our study. In our alcohol dependent participants low MOR availability and the lack of reported alcohol craving may also be a protective factor in reducing the risk of further relapse to alcohol use in these individuals. Unfortunately, we do not have follow-up data to examine this.

143 3.4.2. Associations between MOR availability and clinical variables associated with alcohol use and dependence: severity of alcohol dependence, risk of relapse and total harmful alcohol use

There were no significant associations between MOR availability and severity of alcohol dependence (SADQ scores), risk of relapse (TRQ scores) or lifetime harmful alcohol use.

When completing the Severity of Alcohol Dependence Questionnaire (SADQ) alcohol dependent participants were required to answer questions in relation to their last episode of heavy drinking, which for some individuals was a substantial period of time prior to participating in the study (i.e. up to several years). It is possible that measuring MOR availability closer to dependent alcohol use may reveal a significant association with SADQ scores, although no association has been shown between [11C]diprenorphine binding and SADQ scores in recently abstinent alcohol dependent individuals (Williams et al, 2009). Furthermore, the SADQ contains a large number of items rating symptoms of alcohol withdrawal, for example sweating, shaking and anxiety (Stockwell et al, 1979). As alcohol withdrawal symptoms are primarily mediated through GABA-ergic and glutamatergic signalling (Lingford-Hughes et al, 2012), it might be expected that the withdrawal symptom SADQ items would be less associated with changes in MOR signalling in alcohol dependence.

There is evidence of associations between higher MOR availability and higher risk of relapse in cocaine dependence (Ghitza et al, 2010; Gorelick et al, 2008), but this association has not been investigated in the alcohol dependence literature (Heinz et al, 2005; Weerts et al, 2011; Williams et al, 2009). It has been shown, however, that higher MOR is associated with higher alcohol craving (Heinz et al, 2005; Williams et al, 2009) and this higher craving may represent an increased risk of relapse, although this association is complex (Heinz et al, 2009). Our abstinent alcohol dependent participants had no reported craving and no higher MOR availability and may therefore be less vulnerable to MOR mediated relapse.

Total lifetime harmful alcohol use was calculated as total weeks with mean >60g alcohol daily use. Previous research in alcohol dependence during early abstinence has shown no significant associations between total years of dependent alcohol use and MOR availability,

144 although the criteria for calculating total years of dependent use were not described (Weerts et al, 2011). Williams et al. (2009) found that higher lifetime alcohol consumption, defined as total lifetime kilograms of alcohol consumed, was associated with lower [11C]diprenorphine binding. It is possible that differences in the measure of lifetime alcohol use, for example Williams et al. total kilograms compared with our ‘harmful’ alcohol use measure, is a cause for differences in results. It is also possible that that there are changes in kappa or delta opioid receptor availability associated with cumulative alcohol use which would be detected with non-specific [11C]diprenorphine but not with MOR specific [11C]carfentanil.

Our results suggest that there is no long-term effect of cumulative harmful alcohol use on MOR availability present in individuals with prolonged stable abstinence. Recent heavy alcohol use may have a more significant impact on MOR availability by increasing endogenous opioid release which leads to compensatory changes in MOR and endogenous opioid tone (see Figure 3.6.). In cocaine dependence higher MOR availability has been associated with higher recent cocaine use, measured with urine benzoylecgonine concentration prior to a [11C]carfentanil PET scan following one day of abstinence (Gorelick et al, 2005).

3.4.3. Associations between MOR availability and other clinical variables of interest: Impulsivity, anxiety and depression

Associations between UPPS-P Negative Urgency and Positive Urgency were examined in alcohol dependent participants. Whilst it has previously been shown that in gambling disorder there are correlations between higher MOR availability in the caudate and higher UPPS-P Negative Urgency scores (Mick et al, 2016), we did not replicate this finding in alcohol dependence. There was also no significant correlation between MOR availability and Positive Urgency scores in alcohol dependent participants. Opioid receptor antagonists do not modulate impulsive choices or impulsivity related motor responses in alcohol dependence (Mitchell et al, 2007), which may be consistent with our findings of no correlations between impulsivity scores and MOR availability in our cohort of alcohol dependent participants.

145 There is also a similar lack of similarity between alcohol dependence and gambling disorder in the associations between impulsivity and dopamine receptor availability. In gambling disorder there are correlations between Eysenck Personality Inventory impulsivity and [11C]- (+)-PHNO (D3-preferring) binding and UPPS-P Negative and Positive Urgency and [11C]raclopride (D2/3) binding (Boileau et al, 2013; Clark et al, 2012). However, similar correlations between impulsivity (Barratt Impulsivity Scale) and [11C]-(+)-PHNO are not present in alcohol dependent participants (Erritzoe et al, 2014). These findings may indicate that the relationship between impulsivity and both dopamine and endogenous opioid signalling may differ between alcohol dependence and gambling disorder.

There were no significant correlations between MOR availability and BDI scores in either healthy controls or alcohol dependent participants. A lack of association between MOR availability and BDI scores has also been demonstrated by others in alcohol dependence and in gambling disorder and cocaine dependence (Gorelick et al, 2008; Mick et al, 2016; Weerts et al, 2011). Lower MOR availability has been shown in individuals with major depressive disorder (Kennedy et al, 2006). However, our participants did not have a current diagnosis of depression, and therefore it is difficult to compare our results with individuals who had a current clinical diagnosis of major depressive disorder.

There were also no significant correlations between MOR availability and STAI or SSAI scores in either of our participant groups. This is in keeping with a lack of associations between MOR availability and STAI scores in gambling disorder (Mick et al, 2016), Symptom Check List-90R anxiety ratings in cocaine dependence (Gorelick et al, 2008), or Beck Anxiety Inventory (BAI) scores in alcohol dependence (Weerts et al, 2011).

3.4.4. Examining the potential confounding effect of current smoking and nicotine dependence on MOR availability

A higher proportion of alcohol dependent participants were current smokers compared with healthy controls (54% and 16% respectively). We did not observe any significant effect of current smoking on MOR availability in our healthy controls or alcohol dependent

146 participants, and no interaction between current smoking and alcohol dependent status. There was also no effect of severity of nicotine dependence on MOR availability (as measured with FTND scores) in our current smokers. These results suggest that a higher proportion of current smokers in our alcohol dependent participants compared with healthy controls may not have a confounding effect on our comparisons of MOR availability between these two groups of participants. However, the numbers of current smokers were small, particularly in the healthy control group, and there may be an issue of inadequate power to detect differences in MOR availability due to smoking in our study population.

Three published studies have compared MOR availability between smokers and non-smokers using [11C]carfentanil PET. One of these studies showed lower MOR availability in current smokers (Nuechterlein et al, 2016), whilst the other two showed no differences (Kuwabara et al, 2014; Ray et al, 2011). These studies required their smoking participants to be abstinent from nicotine overnight prior to their [11C]carfentanil PET scans, therefore it is possible that nicotine withdrawal may have affected MOR availability. Our participants were allowed to smoke ad libitum except for one hour prior to the [11C]carfentanil PET scans and therefore nicotine withdrawal would not be influencing their MOR availability.

3.4.5. Examining the potential confounding effect of differences in age on MOR availability

In healthy controls there was a significant positive correlation between putamen [11C]carfentanil binding and age, suggesting older individuals have higher MOR availability. There was a similar trend in healthy controls in five other ROIs (insula, OFc, temporal lobe, anterior cingulate and frontal operculum) which did not survive correction for multiple comparisons, whilst there was a trend of lower hypothalamus MOR availability associated with older age. These findings are similar to those of Zubieta et al. (1999) who found higher MOR availability in the putamen, anterior cingulate, temporal, parietal and pre-frontal cortices associated with older age. They did not examine hypothalamus MOR availability, but did find lower MOR associated with older age in some subcortical structures including

147 amygdala and thalamus. We did not find any significant associations between age and [11C]carfentanil binding in alcohol dependent participants.

If differences in age were confounding our comparison of MOR availability between alcohol dependent participants and healthy controls it would be expected for alcohol dependent participants to have higher MOR availability rather than the trend towards lower MOR availability across all regions seen in our results.

3.4.6. The effects of the OPRM1 A118G polymorphism on MOR availability

There was evidence of lower MOR availability in individuals who were OPRM1 A118G G-allele carriers (G:G homozygous or G:A heterozygous) compared with A-allele homozygous individuals (A:A). Lower MOR availability in G-allele carriers was observed in both healthy controls and alcohol dependent participants, and in a combined group of these individuals. These results were only significant following multiple comparison correction in the ventral pallidum in healthy controls, and the thalamus in the combined group. There was no evidence of a differential effect of the OPRM1 A118G on MOR availability between healthy controls and alcohol dependent participants.

The finding of lower MOR availability in OPRM1 A118G G-allele carriers is consistent with previous publications in healthy controls, smokers and alcohol dependent participants (Domino et al, 2015; Nuechterlein et al, 2016; Peciña et al, 2015b; Ray et al, 2011; Weerts et al, 2013). Whilst the underlying mechanism for the lower MOR availability associated with the OPRM1 G-allele is not well understood (see Chapter 1, Section 1.6. for details), our results suggest the effect of the OPRM1 genotype to modulate MOR availability is not different in alcohol dependence.

148 11 3.4.7. The effect of cold carfentanil mass on [ C]carfentanil BPND

There was a significant positive correlation between cold carfentanil mass and [11C]carfentanil

BPND in the amygdala in healthy controls after correction for multiple comparisons, but there

11 were no other significant correlations between injected mass and [ C]carfentanil BPND in any ROI in either healthy controls or alcohol dependent participants. This finding of higher

11 [ C]carfentanil BPND associated with higher injected mass is unusual as typically it would be expected that higher masses will lead to reduced BPND due to greater occupancy of a target site by the cold compound. Also, if there was an impact of cold carfentanil mass on

11 [ C]carfentanil BPND it would be expected to be observed globally across all ROIs.

11 Colasanti et al. (2012) investigated cold mass effects on [ C]carfentanil BPND in 37 individuals with a range of injected masses from 0.1 to 2.4 μg and did not find any correlations in either cortical or subcortical ROIs. Elsewhere in the published literature, where cold carfentanil mass

11 effects on [ C]carfentanil BPND have been examined, there has also not been any evidence of mass effects at the maximum 0.03 μg/kg cold carfentanil masses typically used (Hirvonen et al, 2009; Mick et al, 2016).

PET radioligands are typically injected in ‘tracer’ doses where the total mass of a compound (hot and cold) will occupy <1% of the target binding sites (Hume et al, 1998). At these low doses it would not be expected for there to be any effect of the cold ligand mass on receptor availability due to the very low proportion of receptors that are occupied (Innis et al, 2007). In our study the maximum injected cold mass was 0.03 μg/kg, with most participants receiving less cold carfentanil than this. An injected carfentanil mass of 0.03 μg/kg has been calculated to occupy between 0.3 to 0.6% of MOR (Greenwald et al, 2003; Harris et al, 2009), which is in line with the <1% occupancy tracer dose and suggests that 0.03 μg/kg of carfentanil is unlikely to have any effect on the occupancy of the available MOR binding sites.

Given the low MOR occupancy of the mass of carfentanil used in this study, the lack of any

11 similar findings of association between cold carfentanil mass and [ C]carfentanil BPND in other studies, and the finding of an association between cold mass and BPND in just one ROI

149 11 out of 21 examined, it is unlikely that there is a significant mass effect on [ C]carfentanil BPND in our data.

This is particularly of interest as there is significantly higher injected cold carfentanil mass in our alcohol dependent participants. Within the healthy control population there is also a difference in the injected cold mass with the lowest mass in the earliest [11C]carfentanil dexamphetamine challenge study conducted in healthy controls (Colasanti et al, 2012). The cold carfentanil mass increased across the studies in chronological order, with the highest mass in the participants in the most recent [11C]carfentanil in alcohol dependence study (including the acetate challenge participants), where there were no significant differences in injected cold mass compared between healthy controls and alcohol dependent participants (see Table 3.11.). During the Colasanti et al. study the Imanova/Invicro CIC radiochemistry department produced [11C]carfentanil with higher specific activity due to concerns about higher cold masses displacing [11C]carfentanil. However, Colasanti et al. (2012) did not show

11 any evidence of a cold mass effect on [ C]carfentanil BPND in a larger collated healthy control dataset from the Imanova/Invicro CIC. Therefore, the CIC radiochemistry department gradually reduced their higher [11C]carfentanil specific activity production leading to higher cold carfentanil masses injected in more recent [11C]carfentanil PET studies.

3.4.8. Limitations

Sample size As stated in the Chapter 2, Section 2.15. our study was designed to be powered to compare

11 our PET measures ([ C]carfentanil BPND and ∆BPND) between alcohol dependent participants and health controls. Based on the power calculations we planned to obtain [11C]carfentanil datasets for 15 alcohol dependent participants, however only 13 were recruited and scanned for the study.

11 The three other studies examining differences in [ C]carfentanil BPND alcohol dependence compared with healthy controls had a range of participant numbers (Heinz et al, 2005; Hermann et al, 2017; Weerts et al, 2011). Our numbers of alcohol dependent participants

150 (n=13) is lower than other studies (Heinz et al. 2005: n=25, Weerts et al. 2011: n=25 and Hermann et al. 2017: n=38), however our numbers of healthy controls (n=32) were slightly higher than one study and considerably higher than two studies (Heinz et al. 2005: n=10, Weerts et al. 2011: n=30 and Hermann et al. 2017: n=10). The study with the largest alcohol dependent cohort (Hermann et al. 2017: n=38) did not show significant differences in MOR availability compared with healthy controls (Hermann et al, 2017). We predicted that 15 participants would be required in each group to adequately detect a 15% higher MOR availability in alcohol dependent participants, and so therefore with only 13 participants our analysis is underpowered. However, given the trend towards lower MOR availability in our alcohol dependent participants across a number of ROIs, it is unlikely that low power is the reason for the failure to show higher MOR availability in alcohol dependence as we predicted in our hypothesis.

Power calculations were not carried out for the other analyses presented in this chapter. Therefore, it is possible that the analyses of the associations of MOR availability with other variables such as current smoking, abstinence from alcohol and severity of dependence are underpowered. For example, examining the effect of OPRM1 genotype on MOR availability there were only five healthy control G-allele carriers and four alcohol dependent G-allele carriers, which is a very low number for examining the effect of the OPRM1 genotype on MOR availability.

Cross-sectional MOR availability data When assessing associations between MOR availability and duration of abstinence from alcohol there was only a single cross-sectional measure available. Repeated longitudinal [11C]carfentanil PET imaging in alcohol dependent participants (e.g. every 3-6 months) over a longer period of abstinence may provide better evidence of whether there are changes in MOR availability with abstinence or not. However, this study is unlikely to be feasible due to the risks related to the radiation exposure from multiple PET scans, and the high cost of the large number of PET scans that would be required in a sample size large enough to account for the rates of relapse to alcohol use.

151 Demographic differences There were significant differences in age between our healthy controls and alcohol dependent participants, and higher proportion of smokers in the alcohol dependent cohort. The majority of healthy controls included in this analysis were not recruited specifically for a comparison with alcohol dependence, with only five recruited alongside the alcohol dependent cohort. The aim with these five healthy controls was to age and smoking status match with the alcohol dependent group who were older and had a higher proportion of current smokers than the previously recruited healthy control cohorts by Dr Liese Mick and Dr Alessandro Colasanti. However, due to the difficulties finding older current smoking healthy controls eligible for the study, the decision was made to primarily focus on matching by age at the expense of matching by smoking status.

In our results we have attempted to examine if differences in age or smoking status may impact on our comparison of MOR availability between alcohol dependent participants and healthy controls. Although we did not find any evidence of an effect of current smoking status on MOR availability or that older age would be leading to differences in MOR availability in alcohol dependent participants compared with healthy controls, we cannot exclude that these demographic differences were confounding the MOR availability results. Therefore, these demographic differences remain as a limitation of this study.

ROI volumes and partial volume effects Whilst there were no significant differences in ROI volumes in our alcohol dependent participants compared with healthy controls (Section 2.10.4.), there was a trend to lower volumes in alcohol dependent participants in the majority of the ROIs. This difference in ROI

11 volumes may lead to a potential bias in [ C]carfentanil BPND values due to partial volume effects (PVEs).

PET imaging has limited resolution due to a number of factors including the distance travelled by positrons before annihilation and smoothing of the PET data in pre-processing (Munk et al, 2017). These factors lead to a ‘blurring’ of the PET signal which can result in both ‘spill-in’ of signal to lower binding regions, and ‘spill-out’ of signal from high binding regions. These

152 effects, known as PVEs, are more pronounced the smaller a brain region is (Rousset et al, 1998) and may lead to an underestimation of specific PET ligand binding in small volume, high binding regions (Bencherif et al, 2004a; Rousset et al, 2000). The smaller volume ROIs in our

11 alcohol dependent participants may lead to greater underestimation of [ C]carfentanil BPND compared with healthy controls due to PVEs, and this is a potential cause of the lower

11 [ C]carfentanil BPND values in our alcohol dependent group (Section 3.3.2.).

There are methods for correcting PVEs, known as partial volume correction (PVC), which include both ROI-wise and voxel-wise methods (Rahmim et al, 2013). Some PVC methods have been shown to potentially provide a better estimate of specific binding, particularly when comparing between participants with higher and lower degrees of atrophy, for example individuals with older age or Parkinson’s disease (Bencherif et al, 2004a; Rousset et al, 2000). However, all PVC methods use assumptions, such as the uniformity of radiotracer binding within regions, or the effects of data pre-processing by the scanner such as scatter correction and smoothing on PVE (Rahmim et al, 2013). There is evidence that some PVC methods may introduce artefacts to the data, and this may affect the quantification of the PET ligand (Munk et al, 2017). Therefore, whilst PVC provides different specific binding values compared with data analysed without PVC, these PVC binding values may be no more representative of ‘actual’ specific binding.

We did not use PVC in our analysis due to the issues outlined in the paragraph above. We did attempt to investigate if there was evidence of significant PVEs in the range of volumes observed in our participants’ ROIs and did not find any correlations between ROI size and

11 [ C]carfentanil BPND (Section 2.10.3.). However, this does not exclude the possibility that there may be bias in our comparison of MOR availability between alcohol dependent participants and healthy controls due to PVEs. This may be an issue in the other published studies examining MOR availability in alcohol dependence which also did not correct for PVEs (Heinz et al, 2005; Weerts et al, 2011; Williams et al, 2009).

One possible method to examine MOR availability in alcohol dependence without the issues of PVEs would be a post-mortem autoradiography study using a similar cohort of healthy controls and long term abstinent alcohol dependent participants. This could provide a better

153 11 insight if our results of lower [ C]carfentanil BPND in alcohol dependence are reproduced by lower autoradiographic MOR availability.

Registration techniques The problems regarding non-linear registration of structural T1 MRI scans to the MNI 152 template in some participants with a large degree of atrophy were discussed in Chapter 2, Section 2.9.3.. The initial problems using SPM8 normalisation in MIAKAT were addressed by using SPM12 unified segmentation instead, and this gave a better visual fit of the non-linear registration, as well as changing [11C]carfentanil values and eliminating some likely erroneous results of negative BPND values in some participants’ ROIs.

SPM8 Normalisation and SPM12 Unified Segmentation are two of a large number of non- linear registration methods. There is evidence of variation across the different methods, with some performing better than others using certain outcome metrics (Klein et al, 2009), but these outcome metrics may not be well justified (Ribeiro et al, 2015). Therefore, quantifying which specific non-linear registration method provides the best fit in a dataset may be difficult.

Manual segmentation of brain ROIs is an alternative to automated methods and is often considered to be the ‘gold standard’ technique (Schoemaker et al, 2018), however there are also issues with this method. Manual segmentation is a time consuming process that requires a considerable level of training and neuroanatomical knowledge, and results may vary depending on the specific method used for defining an ROI (Jack et al, 1995). Even when using the same method there is evidence of both intra- and inter-operator variability (Jack et al, 1995). Due to limitations in time, and lack of resources for trained multi-operator delineation of ROIs in each subject, high-quality reliable manual delineation of the ROIs was not deemed to be a feasible in our dataset.

Other studies examining differences in MOR availability in alcohol dependent participants used a range of methods to define ROIs. Heinz et al. (2005) and Hermann et al. (2017) used an MNI ROI template which was non-linearly transformed to single subject space and then

154 manually ‘adjusted’. Weerts et al. (2011) manually defined three of their eight ROIs and the remainder were from an MNI 152 template atlas non-linearly registered to subject T1 structural MRIs using SPM2 Normalisation. Williams et al. (2009) used SPM2 Normalisation to fit all ROIs. These studies are likely to have similar issues with brain atrophy in alcohol dependent participants, and given that some used non-linear registration methods that may be less reliable than our SPM12 Unified Segmentation (e.g. SPM2 and SPM5 Normalisation) it

11 is unlikely that the [ C]carfentanil BPND data in our results were more affected by atrophy compared with other similar studies.

Simplified reference tissue model (SRTM) The simplified reference tissue model used in our analysis uses an occipital lobe reference region to calculate specific binding in our ROIs (details in Chapter 2, Section 2.9.2.). SRTM assumes that there is no specific radioligand binding in the reference tissue, no differences in the volume of distribution of free radiotracer in the reference tissue compared with target tissues, and that the reference tissue is not affected by pathology when comparing between groups (Lammertsma and Hume, 1996). Differences in MOR in the occipital cortex reference

11 region associated with alcohol dependence may influence our [ C]carfentanil BPND results. However, given the negligible concentrations of MOR in the occipital cortex, it is unlikely that

11 this would have a significant impact on SRTM calculated [ C]carfentanil BPND values.

Measurement of MOR availability.

11 Our baseline [ C]carfentanil BPND is a measurement of ‘MOR availability’, and not a measure

11 of MOR concentration. In-vivo [ C]carfentanil BPND does not bind to receptors that have been internalised (Quelch et al, 2017), or occupied by endogenous ligands. As discussed in Section 3.4.1., our in vivo MOR availability results have to be interpreted in the context of possible changes in both MOR receptor concentrations and endogenous opioid tone.

155 3.5. Conclusion

At the start of this chapter it was hypothesised that abstinent alcohol dependent participants would have higher MOR availability compared with healthy controls, based on a number of [11C]carfentanil and [11C]diprenorphine PET studies in alcohol dependence (Heinz et al, 2005; Hermann et al, 2017; Weerts et al, 2011; Williams et al, 2009). We did not find significantly higher MOR availability in abstinent alcohol dependent participants, and indeed there was a trend towards lower MOR availability in a number of regions (e.g. anterior cingulate and ventral pallidum).

It is possible that the lack of higher MOR availability is due to longer durations of abstinence in our alcohol dependent participants compared to the durations in the other published studies (i.e. months to years compared with days to weeks of abstinence respectively). However, we did not find any significant association between duration of abstinence and MOR availability in our alcohol dependent participants.

We also did not show any significant correlations between MOR availability and clinical variables associated with alcohol dependence including severity of dependence (SADQ scores), risk of relapse (TRQ scores), total harmful alcohol use or impulsivity.

Therefore, we can conclude that long-term abstinent alcohol dependent participants do not have higher MOR availability and MOR availability in these participants does not appear to be associated with alcohol dependence related clinical variables.

156 CHAPTER 4: INVESTIGATING ENDOGENOUS OPIOID TONE IN ALCOHOL DEPENDENCE

4.1. Introduction

4.1.1. Aims

This chapter examines the endogenous opioid release following an oral 0.5mg/kg dexamphetamine challenge in healthy controls and alcohol dependent participants using [11C]carfentanil PET. The aims of this chapter are to: 1. Examine which brain regions show significant endogenous opioid release following an oral dexamphetamine challenge in a large sample of healthy controls 2. Compare oral dexamphetamine-induced endogenous opioid release in healthy controls and alcohol dependent participants. 3. Examine whether factors related to alcohol dependence, for example duration of abstinence, severity of dependence or heavy alcohol use, are associated with differences in oral dexamphetamine-induced endogenous opioid release.

4.1.2. Introduction to endogenous opioid tone results chapter

[11C]carfentanil PET can be used to measure endogenous opioid release following both behavioural and pharmacological challenges (see Chapter 1, Section 1.5.5.). Using this method a measurable endogenous opioid release three hours following an oral 0.5mg/kg dexamphetamine challenge has been demonstrated (Colasanti et al, 2012; Mick et al, 2014). This dexamphetamine-induced endogenous opioid release is blunted in gambling disorder in a number of brain regions, including putamen, insulate and anterior cingulate (Mick et al, 2016).

157 Blunted oral dexamphetamine-induced endogenous opioid release in gambling disorder may reflect an ‘opioid deficient’ state, possibly linked to broader changes in reward sensitivity in addiction (Oswald and Wand, 2004; Ulm et al, 1995). It is important to understand whether blunted endogenous opioid responses are present in other addictions, for example alcohol dependence, as this may indicate that dysregulated opioidergic tone is a common feature of addiction. Subjective ratings of dexamphetamine effects will be investigated to examine if lower endogenous opioid release is associated with lower ‘euphoric’ effects of dexamphetamine.

Alcohol use is associated with endogenous opioid release (Mitchell et al, 2012) and alcohol dependence is associated with changes in MOR availability and MOR concentrations (Heinz et al, 2005; Hermann et al, 2017; Weerts et al, 2011) which may be related to chronic alcohol- induced endogenous opioid release (see Chapter 3, Section 3.4.1. for more details). Therefore, it might be expected that the degree of endogenous opioid tone dysregulation is different in alcohol dependence compared with gambling disorder where there is no pharmacological effect of a substance on endogenous opioid signalling.

Associations between dexamphetamine-induced endogenous opioid release and factors related to alcohol use and dependence and other clinical measures (e.g. duration of heavy alcohol use, duration of abstinence, impulsivity and depressive and anxiety symptoms) will also be examined. As the ventral striatum (NAcc) MOR has been associated with craving in alcohol dependence and shows significant endogenous opioid release after an oral alcohol challenge (Heinz et al, 2005; Mitchell et al, 2012) associations between alcohol dependence related clinical factors and oral dexamphetamine-induced endogenous opioid release in this ROI will be examined.

Plasma dexamphetamine concentrations following the oral dexamphetamine challenge will be compared between alcohol dependent participants and healthy controls to examine whether differences in dexamphetamine pharmacokinetics may be mediating differences in endogenous opioid release. Serum cortisol concentrations will be examined to understand whether other pharmacodynamic effects of the oral dexamphetamine challenge are blunted to a similar degree as endogenous opioid release in abstinent alcohol dependent individuals.

158

Similarly to Chapter 3, associations between oral dexamphetamine-induced endogenous opioid release and other factors which may be potentially confounding oral dexamphetamine-induced endogenous opioid release, including current smoking and the OPRM1 A118G polymorphism will also be investigated.

4.1.3. Hypotheses

3. Alcohol dependent participants will have a blunted endogenous opioid release following the 0.5mg/kg oral dexamphetamine challenge compared with healthy controls. 4. There will be blunted subjective effects of the oral dexamphetamine challenge in alcohol dependent participants compared with healthy controls and more blunted subjective effects will be associated with more blunted endogenous opioid release. 5. Alcohol dependent participants will have a more blunted endogenous opioid release following oral dexamphetamine challenge compared with individuals with gambling disorder. 6. There will be associations between blunted dexamphetamine-induced endogenous opioid release and alcohol dependence related clinical variables including craving, lifetime alcohol use, severity of dependence, risk of relapse and duration of abstinence in alcohol dependent participants.

4.2. Methods

4.2.1. Study population and scanning procedures

Dexamphetamine challenge [11C]carfentanil PET data were available for 20 healthy controls, 13 alcohol dependent and 15 gambling disorder participants. More details are available in Chapter 2, Section 2.1. All participants underwent two [11C]carfentanil PET scans, one before and another three hours following an oral dose of 0.5mg/kg dexamphetamine. Scanning

159 procedures and data analysis were identical for both pre- and post-dexamphetamine [11C]carfentanil PET scans and are described in Chapter 2, Section 2.9..

11 4.2.2. Calculating [ C]carfentanil ∆BPND

11 Endogenous opioid release was indexed as the fractional change in [ C]carfentanil BPND following the dexamphetamine challenge, compared with pre-dexamphetamine challenge

11 [ C]carfentanil BPND, and calculated as follows:

6,-./789−,-./7:;#< ∆,-./ = ,-./789

4.3. Results

4.3.1. Demographics

11 [ C]Carfentanil BPND data before and after a 0.5mg/kg oral dexamphetamine challenge were available for 20 healthy controls, 13 alcohol dependent and 15 gambling disorder participants. The demographic data for these participants are shown in Table 4.1..

As shown in Table 4.1. an ANOVA demonstrated significant differences between the three groups in STAI, BDI, alcohol abstinence, injected carfentanil mass, BIS and UPPS-P positive and negative urgency subscales.

Post-hoc tests showed alcohol dependent participants were significantly older than gambling disorder participants and had a significantly longer duration of abstinence from alcohol than both gambling disorder participants and controls. Gambling disorder participants had significantly higher STAI and BDI scores than controls, and significantly higher BIS scores than both controls and alcohol dependent participants. Both alcohol dependent and gambling

160 disorder participants had significantly higher UPPS-P negative and positive urgency scores than healthy controls.

The highest prevalence of current smokers was in in alcohol dependent participants, with the lowest prevalence in the healthy controls. However, within current smokers there were no significant differences in FTND scores, cigarettes per day or pack years between groups.

Pre-and post-dexamphetamine injected cold carfentanil mass was higher in alcohol dependent participants compared with healthy controls.

161 Table 4.1. – Demographic measures (mean ±SD) compared between healthy controls (HC), alcohol dependent (AD) and gambling disorder (GD) participants and including results from ANOVA examining if there are significant differences between groups and post-hoc independent sample t-tests or Mann Whitney U test for BDI and alcohol abstinence. Data shown for 20 healthy controls, 13 alcohol dependent and 13 gambling disorder participants unless otherwise indicated. HC AD GD ANOVA Variable (total 20) (total 13) (Total 15) p value 46.6 (±7.3) 34.3 (±7.3) Age 38.9 (±11.3) 0.004 † † BMI 25.0 (±4.4) 26.7 (±3.7) 26.8 (±5.0) 0.475 (14 HC) Alcohol abstinence (days) 8.4 (±11.4) 605 (±867) 8.1 (±8.1) 0.003 (14 HC) * *, † † Gambling abstinence (days) N/A N/A 47 (±40.8) N/A 3 7 4 Current Smokers N/A (15%) (54%) (27%) Cigarettes per day in smokers 10.0 (±5.0) 10.6 (±7.7) 13.5 (±9.4) 0.797 (7 AD, 3 HC, 4 GD) FTND in smokers 3.0 (±2.6) 3.7 (±3.1) 5.3 (±1.7) 0.530 (7 AD, 3 HC, 4 GD) Pack years in current and ex-smokers 7.9 (±8.1) 23.6 (±14.5) 16.0 (±6.4) 0.110 (11 AD, 4 HC, 4 GD) STAI 29.6 (±6.9) 44.5 (±12.7) 37.2 (±5.9) <0.001 (19 HC) ** ** 0.6 (±1.6) 3.3 (±3.6) 8.1 (±8.0) BDI <0.001 ** ** BIS total score 50.1 (±7.4) 49.2 (±4.8) 70.8 (±10.6) <0.001 (17 HC) ** † **, † 21.0 (±5.6) 27.4 (±4.1) 32.3 (±5.7) Negative Urgency <0.001 *, ** * ** Lack of 21.8 (±4.9) 22.8 (±3.5) 24.3 (±5.7) 0.374 premeditation UPPS-P Lack of (12 AD, 14 HC) 18.5 (±4.0) 18.5 (±3.8) 20.5 (±4.5) 0.351 perseverance Sensation Seeking 31.9 (±8.5) 33.1 (±6.0) 35.2 (±6.8) 0.472 21.4 (±6.3) 30.8 (±4.9) 28.3 (±8.3) Positive Urgency 0.003 *, ** * ** Injected carfentanil mass pre- 1.2 (±0.6) 1.7 (±0.5) dexamphetamine 1.37 (±0.32) 0.023 * * (19 HC) Injected carfentanil mass post- 1.2 (±0.5) 1.7 (±0.4) dexamphetamine 1.31 (±0.20) 0.016 * * (19 HC) Post-hoc tests: * HC vs. AD Bonferroni corrected significance threshold p<0.017 ** HC vs. GD Bonferroni corrected significance threshold p<0.017 † AD vs. GD Bonferroni corrected significance threshold p<0.017

162 11 4.3.2. Changes in [ C]carfentanil BPND following 0.5mg/kg oral dexamphetamine challenge in healthy controls

A repeated measures ANOVA examining the effect of the oral dexamphetamine challenge on

11 [ C]carfentanil BPND in 20 healthy controls showed a significant within-subject effect of Scan (pre- and post-dexamphetamine challenge) (Table 4.2.).

Table 4.2. – Repeated measures ANOVA examining the within-subject effects of Scan (pre- and post-

11 dexamphetamine) and ROI (21 regions of interest) on [ C]carfentanil BPND in 20 healthy controls. Effects F-ratio (effect df, error df) p value Scan 105.5 (1, 19) <0.001 Within-subject effects ROI 228.2 (4.1, 76.9) <0.001 Scan x ROI 6.0 (4.4, 83.1) <0.001

The significant within-subject effect of Scan and Scan x ROI interaction were further investigated post-hoc using paired sample t-tests for each of the 21 ROIs. Post-hoc t-tests

11 showed significant reductions in [ C]carfentanil BPND after the oral dexamphetamine challenge in all 21 ROIs (Hochberg P-plot correction for multiple comparisons) (Table 4.3.).

11 There was a range in the magnitude of the reduction in [ C]carfentanil BPND across the 21 ROIs with the largest reduction in the globus pallidus (8.9% reduction) and the smallest in the precentral gyrus (3.5% reduction).

163 11 Table 4.3. – Mean (±SD) pre- and post-dexamphetamine [ C]carfentanil BPND with p values from paired sample t-test in 20 healthy controls.

11 Mean (±SD) [ C]carfentanil BPND Mean (±SD) ROI Pre- Post- [11C]carfentanil p value dexamphetamine dexamphetamine ∆BPND NAcc 2.83 (±0.35) 2.65 (±0.35) 0.063 (±0.052) <0.001 * Amygdala 1.73 (±0.22) 1.66 (±0.19) 0.039 (±0.060) 0.008 * Anterior cingulate 1.46 (±0.18) 1.39 (±0.18) 0.050 (±0.028) <0.001 * Caudate 1.37 (±0.34) 1.27 (±0.35) 0.075 (±0.066) <0.001 * Cerebellum 0.85 (±0.27) 0.80 (±0.26) 0.055 (±0.046) <0.001 * DLPFC 1.15 (±0.17) 1.08 (±0.16) 0.057 (±0.037) <0.001 * Frontal operculum 1.27 (±0.17) 1.22 (±0.19) 0.040 (±0.045) <0.001 * Globus pallidus 1.40 (±0.25) 1.28 (±0.27) 0.089 (±0.069) <0.001 * Hippocampus 0.71 (±0.10) 0.68 (±0.10) 0.049 (±0.065) 0.004 * Hypothalamus 1.85 (±0.41) 1.72 (±0.42) 0.070 (±0.088) 0.005 * Insula 1.48 (±0.19) 1.41 (±0.20) 0.050 (±0.036) <0.001 * MPFC 1.12 (±0.18) 1.06 (±0.18) 0.053 (±0.043) <0.001 * Orbitofrontal 1.37 (±0.20) 1.29 (±0.19) 0.061 (±0.045) <0.001 * Parietal lobe 0.78 (±0.11) 0.74 (±0.11) 0.049 (±0.030) <0.001 * Posterior cingulate 0.85 (±0.14) 0.80 (±0.13) 0.056 (±0.054) <0.001 * Precentral G. 0.75 (±0.12) 0.72 (±0.11) 0.035 (±0.067) 0.023 * Putamen 1.85 (±0.24) 1.73 (±0.28) 0.062 (±0.042) <0.001 * SMA 1.07 (±0.19) 1.03 (±0.18) 0.039 (±0.054) 0.004 * Temporal lobe 1.12 (±0.16) 1.07 (±0.15) 0.049 (±0.030) <0.001 * Thalamus 1.70 (±0.22) 1.61 (±0.23) 0.058 (±0.032) <0.001 * Ventral pallidum 2.67 (±0.36) 2.47 (±0.29) 0.070 (±0.069) <0.001 * Bonferroni corrected significance threshold p<0.0024 *Significant test following P-plot Hochberg correction

11 4.3.3. Changes in [ C]carfentanil BPND following 0.5mg/kg oral dexamphetamine challenge in alcohol dependent participants

A repeated measures ANOVA examining the effect of the oral dexamphetamine challenge on

11 [ C]carfentanil BPND in 13 alcohol dependent participants showed no significant within- subject effects of Scan or Scan x ROI interaction (Table 4.4.).

164 Table 4.4. – Repeated measures ANOVA examining the within-subject effects of Scan (pre- and post-

11 dexamphetamine) and ROI (21 regions of interest) on [ C]carfentanil BPND in 13 alcohol dependent participants. Effects F-ratio (effect df, error df) p value Within-subject effects Scan 0.4 (1, 12) 0.537 ROI 137.5 (3.7, 44.3) <0.001 Scan x ROI 1.4 (3.0, 36.5) 0.271

Exploratory post-hoc paired sample t-tests were carried out to examine if there were any ROIs

11 where changes in [ C]carfentanil BPND were significant despite the lack of a significant within- subject effect of Scan. These post-hoc tests showed no individual ROI with significant changes

11 in [ C]carfentanil BPND following dexamphetamine challenge (Table 4.5.). Furthermore, the

11 direction of change in [ C]carfentanil BPND following dexamphetamine challenge varies between ROIs in alcohol dependent participants with five ROIs showing a reduction in BPND and 16 ROIs showing an increase in BPND (see ∆BPND values in Table 4.5.).

165 11 Table 4.5. – Mean (±SD) pre- and post-dexamphetamine [ C]carfentanil BPND with p values from paired sample t-test in 13 alcohol dependent participants (no significant t-test results).

11 Mean (±SD) [ C]carfentanil BPND Mean (±SD) ROI Pre- Post- [11C]carfentanil p value dexamphetamine dexamphetamine ∆BPND NAcc 2.64 (±0.31) 2.66 (±0.37) -0.007 (±0.064) 0.658 Amygdala 1.57 (±0.19) 1.56 (±0.18) 0.004 (±0.078) 0.745 Anterior cingulate 1.37 (±0.11) 1.40 (±0.18) -0.020 (±0.062) 0.204 Caudate 1.30 (±0.26) 1.26 (±0.29) 0.029 (±0.077) 0.247 Cerebellum 0.79 (±0.17) 0.80 (±0.16) -0.016 (±0.089) 0.680 DLPFC 1.04 (±0.10) 1.06 (±0.13) -0.019 (±0.062) 0.298 Frontal operculum 1.23 (±0.10) 1.24 (±0.13) -0.007 (±0.065) 0.717 Globus pallidus 1.34 (±0.20) 1.32 (±0.17) 0.012 (±0.059) 0.411 Hippocampus 0.64 (±0.12) 0.65 (±0.11) -0.007 (±0.109) 0.978 Hypothalamus 1.57 (±0.41) 1.56 (±0.43) 0.004 (±0.102) 0.808 Insula 1.43 (±0.13) 1.46 (±0.18) -0.021 (±0.070) 0.271 MPFC 1.04 (±0.14) 1.04 (±0.17) -0.003 (±0.080) 0.858 Orbitofrontal 1.31 (±0.12) 1.33 (±0.16) -0.012 (±0.053) 0.418 Parietal lobe 0.71 (±0.07) 0.73 (±0.09) -0.021 (±0.079) 0.395 Posterior cingulate 0.75 (±0.10) 0.78 (±0.11) -0.029 (±0.075) 0.183 Precentral G. 0.70 (±0.07) 0.72 (±0.09) -0.034 (±0.079) 0.170 Putamen 1.83 (±0.16) 1.82 (±0.17) 0.003 (±0.054) 0.805 SMA 1.00 (±0.17) 1.04 (±0.20) -0.036 (±0.087) 0.200 Temporal lobe 1.07 (±0.08) 1.09 (±0.10) -0.012 (±0.061) 0.497 Thalamus 1.58 (±0.17) 1.59 (±0.17) -0.012 (±0.071) 0.603 Ventral pallidum 2.36 (±0.26) 2.46 (±0.36) -0.043 (±0.115) 0.220

11 4.3.4. Changes in [ C]carfentanil BPND following 0.5mg/kg oral dexamphetamine challenge compared between healthy controls and alcohol dependent participants

A mixed model ANOVA comparing the effects of the oral dexamphetamine challenge on

11 [ C]carfentanil BPND between 20 healthy controls and 13 alcohol dependent participants showed significant Scan x Status and Scan x Status x ROI interactions (Table 4.6.).

166 Table 4.6. - Mixed model ANOVA examining the within-subject effects of Scan (pre- and post- dexamphetamine) and ROI (21 regions) and between-subject effect of Status (alcohol dependent or healthy

11 control) on [ C]Carfentanil BPND in 20 healthy controls and 13 alcohol dependent participants. Effects F-ratio (effect df, error df) p value Scan 12.3 (1, 31) 0.001 Scan x Status 23.7 (1, 31) <0.001 ROI 345.0 (4.5, 138.6) <0.001 Within-subject factors ROI x Status 1.0 (4.5, 138.6) 0.388 Scan x ROI 2.5 (4.2, 130.7) 0.042 Scan x ROI x 3.9 (4.2, 130.7) 0.004 Status Between-subject factors Status 1.1 (1, 31) 0.313

The significant Scan x Status and Scan x Status x ROI interactions from the mixed model ANOVA were examined further with another mixed model ANOVA examining if there was a

11 difference in [ C]carfentanil ∆BPND values (i.e. endogenous opioid release) between healthy controls and alcohol dependent participants. There was a significant between-subject effect

11 of Status on [ C]carfentanil ∆BPND values (Table 4.7.).

Table 4.7. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between-

11 subject effect of Status (alcohol dependent or healthy control) on [ C]Carfentanil ∆BPND in 20 healthy controls and 13 alcohol dependent participants. Effects F-ratio (effect df, error df) p value Within-subject factors ROI 2.2 (5.9, 183.6) 0.044 ROI x Status 0.8 (5.9, 183.6) 0.574 Between-subject factors Status 20.4 (1, 31) <0.001

11 Post-hoc independent samples t-tests comparing [ C]Carfentanil ∆BPND between healthy controls and alcohol dependent participants were carried out to further examine the significant ANOVA between-subject effect of Status. The post hoc t-tests showed significantly

11 lower [ C]carfentanil ∆BPND values in alcohol dependent participants compared with healthy controls in 17 out of 21 ROIs (Hochberg P-plot correction for multiple comparisons) (Table 4.8. and Figure 4.1.).

167 11 Table 4.8. – Mean (±SD) dexamphetamine-induced [ C]carfentanil ∆BPND compared between 20 healthy controls and 13 alcohol dependent participants (with p value from independent sample t-tests). Mean (±SD) [11C]carfentanil ∆BPND ROI p value Healthy Alcohol Controls dependent NAcc 0.063 (±0.052) -0.007 (±0.064) 0.002 * Amygdala 0.039 (±0.060) 0.004 (±0.078) 0.153 Anterior cingulate 0.050 (±0.028) -0.020 (±0.062) <0.001 * Caudate 0.075 (±0.066) 0.029 (±0.077) 0.077 Cerebellum 0.055 (±0.046) -0.016 (±0.089) 0.005 * DLPFC 0.057 (±0.037) -0.019 (±0.062) <0.001 * Frontal operculum 0.040 (±0.045) -0.007 (±0.065) 0.009 * Globus pallidus 0.089 (±0.069) 0.012 (±0.059) 0.002 * Hippocampus 0.049 (±0.065) -0.007 (±0.109) 0.073 Hypothalamus 0.070 (±0.088) 0.004 (±0.102) 0.059 Insula 0.050 (±0.036) -0.021 (±0.070) 0.001 * MPFC 0.053 (±0.043) -0.003 (±0.080) 0.012 * Orbitofrontal 0.061 (±0.045) -0.012 (±0.053) <0.001 * Parietal lobe 0.049 (±0.030) -0.021 (±0.079) 0.001 * Posterior 0.056 (±0.054) -0.029 (±0.075) 0.001 * cingulate Precentral G. 0.035 (±0.067) -0.034 (±0.079) 0.012 * Putamen 0.062 (±0.042) 0.003 (±0.054) 0.001 * SMA 0.039 (±0.054) -0.036 (±0.087) 0.004 * Temporal lobe 0.049 (±0.030) -0.012 (±0.061) 0.001 * Thalamus 0.058 (±0.032) -0.012 (±0.071) 0.001 * Ventral pallidum 0.070 (±0.069) -0.043 (±0.115) 0.001 * Bonferroni corrected significance threshold p<0.0024 *Significant test following P-plot Hochberg correction

168 Endogenous opioid release

Ventral pallidum * Thalamus * Temporal lobe * SMA * Healthy Control Alcohol Dependent Putamen * Precentral G. * Posterior cingulate * Parietal lobe * Orbitofrontal * MPFC * Insula * Hypothalamus Hippocampus Globus pallidus * Frontal operculum * DLPFC * Cerebellum * Caudate Anterior cingulate * Amygdala NAcc * -0.04 0.00 0.04 0.08 0.12 0.16 0.20 [¹¹C]carfentanil ∆BP ND 11 Figure 4.1. – Mean (±SD) [ C]Carfentanil ∆BPND values in healthy controls and alcohol dependent participants across 21 high-binding ROIs (*significant independent sample t-test following P-plot Hochberg correction for multiple comparison).

11 4.3.5. Changes in [ C]carfentanil BPND following 0.5mg/kg oral dexamphetamine challenge compared between alcohol dependent and gambling disorder participants

A mixed model ANOVA was carried out to examine if there were significant differences in oral

11 dexamphetamine-induced [ C]carfentanil ∆BPND compared between 13 alcohol dependent

169 and 15 gambling disorder participants. This showed no significant effect of Status on

11 [ C]carfentanil ∆BPND (Table 4.9)

Table 4.9. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between-

11 subject effect of Status (alcohol dependent or gambling disorder) on [ C]carfentanil ∆BPND in 13 alcohol dependent and 15 gambling disorder participants. Effect F-ratio (effect df, error df) p value Within-subject factors ROI 1.5 (4.7, 123.0) 0.185 ROI x Status 0.7 (4.7, 123.0) 0.643 Between-subject factors Status 0.8 (1,26) 0.391

Further exploratory post-hoc independent sample t-tests did not show any significant

11 differences in [ C]carfentanil ∆BPND between alcohol dependent and gambling disorder participants within any individual ROIs (Table 4.10., Figure 4.2.).

11 Table 4.10. – Mean (±SD) oral dexamphetamine-induced [ C]carfentanil ∆BPND compared between 13 alcohol dependent and 15 gambling disorder participants with p values from independent sample t-test (no significant t-tests).

11 Mean (±SD) [ C]carfentanil ∆BPND ROI Alcohol Gambling p value dependent disorder NAcc -0.007 (±0.064) 0.028 (±0.045) 0.102 Amygdala 0.004 (±0.078) -0.004 (±0.063) 0.782 Anterior cingulate -0.020 (±0.062) 0.004 (±0.037) 0.228 Caudate 0.029 (±0.077) 0.026 (±0.091) 0.924 Cerebellum -0.016 (±0.089) 0.007 (±0.053) 0.404 DLPFC -0.019 (±0.062) 0.006 (±0.049) 0.251 Frontal operculum -0.007 (±0.065) 0.006 (±0.050) 0.536 Globus pallidus 0.012 (±0.059) 0.000 (±0.105) 0.707 Hippocampus -0.007 (±0.109) -0.011 (±0.081) 0.912 Hypothalamus 0.004 (±0.102) 0.014 (±0.156) 0.852 Insula -0.021 (±0.070) -0.001 (±0.042) 0.374 MPFC -0.003 (±0.080) 0.004 (±0.045) 0.751 Orbitofrontal -0.012 (±0.053) 0.003 (±0.051) 0.469 Parietal lobe -0.021 (±0.079) -0.014 (±0.055) 0.793 Posterior cingulate -0.029 (±0.075) 0.002 (±0.057) 0.227 Precentral G. -0.034 (±0.079) -0.016 (±0.053) 0.498 Putamen 0.003 (±0.054) 0.023 (±0.047) 0.294 SMA -0.036 (±0.087) -0.003 (±0.050) 0.226 Temporal lobe -0.012 (±0.061) -0.006 (±0.037) 0.731 Thalamus -0.012 (±0.071) 0.024 (±0.046) 0.118 Ventral pallidum -0.043 (±0.115) 0.012 (±0.085) 0.157

170 Endogenous opioid release

Ventral pallidum Thalamus Temporal lobe SMA Alcohol Dependent Gambling Disorder Putamen Precentral G. Posterior cingulate Parietal lobe Orbitofrontal MPFC Insula Hypothalamus Hippocampus Globus pallidus Frontal operculum DLPFC Cerebellum Caudate Anterior cingulate Amygdala NAcc -0.04 0.00 0.04 0.08 0.12 0.16 0.20 [¹¹C]carfentanil ∆BP ND 11 Figure 4.2. – Mean (±SD) [ C]Carfentanil ∆BPND values in 13 alcohol dependent and 15 gambling disorder participants across 21 ROIs (independent sample t-test, all uncorrected p>0.05).

4.3.6. Plasma dexamphetamine pharmacokinetics

Plasma dexamphetamine concentrations were collected in 14 healthy control participants and 13 alcohol dependent participants.

In healthy controls a repeated measures ANOVA showed a significant within-subject effect of Time on plasma dexamphetamine concentrations (F(2.7, 35.0)=103.6, p<0.001). Post-hoc paired sample t-tests showed significantly elevated plasma dexamphetamine concentrations

171 at 60, 120, 180 and 270 mins post-dexamphetamine administration compared with baseline (Bonferroni corrected significance threshold p<0.013), with the highest mean plasma concentration measured at three hours following the oral dexamphetamine dose (Figure 4.3.).

In alcohol dependent participants a repeated measures ANOVA also showed a significant effect of Time on plasma dexamphetamine concentrations (F(2.5,29.4)=37.0, p<0.001). Post- hoc paired t-tests showed significantly elevated plasma dexamphetamine concentrations at 60, 120, 180 and 270 minutes post-dexamphetamine administration compared with baseline (Bonferroni corrected significance threshold p<0.013), with the highest mean plasma concentration at three hours post-dexamphetamine dose (Figure 4.3.).

A mixed model ANOVA was used to examine if there were differences in the plasma dexamphetamine concentrations between healthy controls and alcohol dependent participants. This showed a significant between-subject effect of Status on plasma dexamphetamine concentrations, but no significant Time x Status interaction (Table 4.11.).

Table 4.11. – Mixed model ANOVA examining the within-subject effect of Time (pre-dexamphetamine and 60, 120, 180 and 270 mins post-dexamphetamine) and between-subject effect of Status (healthy control or alcohol dependent) on plasma dexamphetamine concentrations in 14 healthy controls and 13 alcohol dependent participants. Effects F-ratio (effect df, error df) p value Within-subject factors Time 121.1 (2.6, 66.1) <0.001 Time x Status 1.6 (2.6, 66.1) 0.203 Between-subject factors Status 5.2 (1, 25) 0.031

To further examine this significant between-subject effect of Status, post-hoc independent sample t-tests were carried out to compare dexamphetamine plasma concentrations between healthy controls and alcohol dependent participants at each time point. These showed a trend of lower plasma dexamphetamine concentrations in alcohol dependent participants at 120 mins and 270 mins following dexamphetamine. However, these were not significant following correction for multiple comparisons (Bonferroni corrected significance threshold p<0.01, Table 4.12.).

172 Table 4.12. – Mean (±SD) plasma dexamphetamine concentrations pre- and post-dexamphetamine challenge in 14 healthy controls and 13 alcohol dependent participants with results from independent sample t-tests (No significant results at Bonferroni corrected significance threshold p<0.01).

Time post Mean plasma dexamphetamine dexamphetamine concentration ng/ml (±SD) p value administration (minutes) Healthy control Alcohol dependent 0 (pre-dexamphetamine) 0.0 (±0.0) 0.0 (±0.0) 0.139 60 48.6 (±22.1) 34.4 (±27.5) 0.150 120 78.2 (±17.2) 58.8 (±25.9) 0.030 180 80.8 (±16.8) 67.5 (±25.3) 0.119 270 76.2 (±8.2) 63.7 (±20.3) 0.044

Healthy Control Alcohol Dependent

100 * *

concentra:on ng/ml

Plasma amphetamine 20 Scan

0 0 60 120 180 240 300 Time (mins)

Figure 4.3. – Mean (±SD) plasma dexamphetamine concentrations (ng/ml) in 14 healthy controls and 13 alcohol dependent participants: prior to and following an oral 0.5mg/kg dexamphetamine challenge (*independent sample t-test p<0.05).

4.3.7. Associations between plasma dexamphetamine concentrations and

11 [ C]carfentanil ∆BPND

11 The association between plasma dexamphetamine concentrations and [ C]carfentanil ∆BPND was explored using Pearson’s correlations. Plasma dexamphetamine concentrations at 180 mins post-dose (i.e. start of the post dexamphetamine [11C]carfentanil PET scan) were

173 11 correlated with NAcc [ C]carfentanil ∆BPND. There were no significant correlations between

11 plasma dexamphetamine concentrations at 180 mins and NAcc [ C]carfentanil ∆BPND when examining healthy controls (R=-0.035, p=0.905) or alcohol dependent participants (R=0.134, p=0.664) separately, or a combined sample of participants (R=0.223, p=0.263).

11 Associations between plasma dexamphetamine concentrations and [ C]carfentanil ∆BPND were further explored by examining correlations between plasma dexamphetamine

11 concentrations at 180 mins post dose and [ C]carfentanil ∆BPND in all other ROIs that showed

11 significantly blunted [ C]carfentanil ∆BPND in alcohol dependent participants (17 ROIs, see Section 4.3.4. for details). Again there were no significant correlations between plasma

11 dexamphetamine concentrations at 180 mins post dose and [ C]carfentanil ∆BPND in any ROI (all uncorrected p>0.05) in healthy controls or alcohol dependent participants separately, or in a combined sample of participants.

Finally, plasma dexamphetamine concentration area under the curve (AUC) values (dexamphetamine ng/ml x mins) were calculated for each participant. Two AUC values were calculated, one for the entire duration of dexamphetamine concentration data (0 to 270 mins) and one for only the duration of the post-dexamphetamine [11C]carfentanil PET scan (180 to

11 270 mins only). The associations between the AUC values and NAcc [ C]carfentanil ∆BPND were examined using Pearson’s correlation coefficient and did not show any significant correlations in healthy controls (0-270 mins AUC R=0.017, p=0.953 and 180-270 mins AUC R=0.011, p=0.971) or alcohol dependent participants (0-270 mins AUC R=0.085, p= 0.783 and 180-270 mins AUC R=0.090, p=0.771). Further exploratory correlational analyses in healthy controls and alcohol dependent participants did not show any significant correlations

11 between 0-270 mins AUC or 180-270 mins AUC and [ C]carfentanil ∆BPND in the remaining 17 ROIs where endogenous opioid release was blunted in alcohol dependent participants (see paragraph above for details).

174 4.3.8. Individual serum cortisol responses to the oral dexamphetamine challenge

Serum cortisol concentrations were collected in five healthy controls and 13 alcohol dependent participants. There was one healthy control with unusual cortisol concentrations which were very high particularly prior to and immediately following oral dexamphetamine dosing (see Figure 4.4.). This participant was excluded from further analysis due to these outlier serum cortisol concentrations.

Figure 4.4. – Individual serum cortisol 800 Scan concentrations (ng/ml) in 5 healthy controls: 600 pre- and post-dexamphetamine challenge.

400

Serum Cor8sol 200 Concentra8on ng/ml

0 0 30 60 90 120 150 180 Time (mins)

In healthy controls a repeated measures ANOVA showed no significant effect of Time (F(1.3, 3.9)=6.3, p=0.065) on serum cortisol concentrations. However, exploratory paired sample t- tests showed significantly higher serum cortisol concentration at 120 mins post- dexamphetamine dose compared with baseline (Bonferroni corrected significance threshold p<0.008).

In alcohol dependent participants there was a range of individual cortisol responses to the oral dexamphetamine challenge with a suggestion of a very limited response in some participants which is most clearly seen at 90-120 mins post-dexamphetamine challenge (Figure 4.5.).

175 Figure 4.5. – Individual serum cortisol 800 Scan concentrations (ng/ml) in alcohol dependent 600 participants: pre- and post-dexamphetamine challenge. 400

Serum Cor8sol 200

Concentra8on ng/ml

0 0 30 60 90 120 150 180 Time (mins)

A repeated measures ANOVA using the whole alcohol dependent sample (n=13) showed a significant effect of Time (F(2.4, 28.3)= 25.1, p<0.001) on serum cortisol concentration. Post- hoc t-tests showed significantly higher serum cortisol concentrations at 90, 120, 150 and 180 mins compared with baseline (Bonferroni corrected significance threshold p<0.008).

4.3.9. Examining possible ‘high’ and ‘low’ cortisol responses in alcohol dependent participants

To explore this potential differential cortisol response to the dexamphetamine challenge in alcohol dependent participants a cut-off of serum cortisol concentration <200ng/ml at 90 mins post-dexamphetamine challenge was used to select ‘low-cortisol responders, and >200ng/ml for ‘high-cortisol responders. A mixed model ANOVA was used to examine if there was a significant difference in serum cortisol response to oral dexamphetamine between the observed possible ‘high’ and ‘low’ cortisol responders in the alcohol dependent group. The mixed model ANOVA showed a significant effect of ‘High’/’Low’ responder status on serum cortisol concentration (Table 4.13.).

176 Table 4.13. – Mixed model ANOVA examining the within-subject effect of Time (pre-dexamphetamine and 30, 60, 90, 120, 150 and 180 mins post-dexamphetamine) and between-subject effect of ‘High’/’Low’ responder (‘high’- and ‘low’-cortisol responder) on serum cortisol concentration in 8 ‘high’- and 5 ‘low’- cortisol responding alcohol dependent participants. Effects F-ratio (effect df, error df) p value Time 38.2 (2.5, 27.4) <0.001 Time x Within-subject factors ‘High’/’Low’ 11.3 (2.5, 27.4) <0.001 responder ‘High’/’Low’ Between-subject factors 19.2 (1, 11) 0.001 responder

Post-hoc independent sample t-tests showed significantly lower serum cortisol concentrations in ‘low-cortisol responders’ at 60, 90, 120 and 150 mins post- dexamphetamine challenge compared with ‘high-cortisol responders’ (Bonferroni corrected significance threshold p<0.007, Figure 4.6.).

‘High’ Cor8sol Releaser Figure 4.6. – Mean (±SD) serum ‘Low’ Cor8sol Releaser cortisol (ng/ml) concentrations in 8 800 Scan ‘high’ and 5 ‘low’ cortisol responding 600 alcohol dependent participants: pre- * * * * and post-dexamphetamine challenge 400 (*significant independent sample t-

Serum Cor8sol 200 test Bonferroni corrected significance Concentra8on ng/ml threshold p<0.007). 0 0 30 60 90 120 150 180 Time (mins)

To explore if alcohol dependent participants with ‘low’ cortisol responses also had lower

11 [ C]carfentanil ∆BPND responses to the oral dexamphetamine challenge, independent sample

11 t-tests were carried out to compare [ C]carfentanil ∆BPND between ‘low’- and ‘high’-cortisol releasing alcohol dependent participants. There was no evidence of significant differences in

11 [ C]carfentanil ∆BPND in any ROI between ‘high’ and ‘low’ cortisol responding alcohol dependent participants (all uncorrected p>0.05, Figure 4.7.).

177

Endogenous opioid release

Ventral pallidum Thalamus Temporal lobe ‘High’ CorSsol Releaser SMA ‘Low’ CorSsol Releaser Putamen Precentral G. Posterior cingulate Parietal lobe Orbitofrontal MPFC Insula Hypothalamus Hippocampus Globus pallidus Frontal operculum DLPFC Cerebellum Caudate Anterior cingulate Amygdala NAcc -0.20 -0.16 -0.12 -0.08 -0.04 0.00 0.04 [¹¹C]carfentanil ∆BP ND 11 Figure 4.7. – Mean (±SD) [ C]Carfentanil ∆BPND values in 8 ‘high’ and 5 ‘low’ cortisol releasing alcohol dependent participants (independent sample t-test, all uncorrected p>0.05).

4.3.10. Comparing serum cortisol concentrations between healthy controls and alcohol dependent participants

A mixed model ANOVA examining if there was a significant difference in serum cortisol responses between healthy controls and all alcohol dependent participants (combining both ‘high’ and ‘low’ cortisol responders) showed no significant between-subject effect of Status (Table 4.14.).

178

Table 4.14. – Mixed model ANOVA examining the within-subject effect of Time (pre-dexamphetamine and 30, 60, 90, 120, 150 and 180 mins post-dexamphetamine) and between-subject effect of Status (healthy control or alcohol dependent) on serum cortisol concentration in 4 healthy controls and 13 alcohol dependent participants. Effects F-ratio (effect df, error df) p value Time 25.1 (2.4, 36.3) <0.001 Within-subject factors Time x Status 0.4 (2.4, 36.3) 0.725 Between-subject factors Status 1.6 (1, 15) 0.225

However, a further mixed model ANOVA showed a significant difference in serum cortisol response between healthy controls and ‘low’-cortisol releasing alcohol dependent participants (Table 4.15.)

Table 4.15. – Mixed model ANOVA examining the within-subject effect of Time (pre-dexamphetamine and 30, 60, 90, 120, 150 and 180 mins post-dexamphetamine) and between-subject effect of Status (healthy control or ‘low’-cortisol releasing alcohol dependent participant) on serum cortisol concentration in 4 healthy controls and 5 ‘low’-cortisol releasing alcohol dependent participants. Effects F-ratio (effect df, error df) p value Time 16.5 (1.5, 10.6) 0.001 Within-subject factors Time x Status 3.2 (1.5, 10.6) 0.089 Between-subject factors Status 44.7 (1, 7) <0.001

Post-hoc independent sample t-tests showed significantly lower serum cortisol concentrations in ‘low’-cortisol releasing alcohol dependent participants compared with healthy controls at 120 and 150 mins following dexamphetamine challenge (Bonferroni corrected significance threshold p<0.007, Figure 4.8.).

179 Alcohol Dependent Figure 4.8. – Mean (±SD) serum ‘High’ cor8sol releasers cortisol (ng/ml) concentrations in 4 Alcohol Dependent healthy controls and 8 ‘high’- and 5 ‘Low’ cor8sol releasers Healthy Controls ‘low’-cortisol responding alcohol 800 Scan dependent participants: pre- and 600 post-dexamphetamine challenge * * (*significant independent sample t- 400 test comparing healthy controls and

Serum Cor8sol low-cortisol responding alcohol 200 Concentra8on ng/ml dependent participants, Bonferroni 0 corrected significance threshold 0 30 60 90 120 150 180 Time (mins) p<0.007).

4.3.11. Subjective effects of oral dexamphetamine challenge in healthy controls: SAIRS scores

SAIRS scale scores were available for 20 healthy controls. A repeated measures ANOVA examining the effect of the oral dexamphetamine challenge on subjective SAIRS ratings across all subscales in healthy controls showed significant effects of Time and SAIRS subscale and a significant Time x SAIRS subscale interaction (Table 4.16.). These significant results were further examined in healthy controls with four further repeated measures ANOVAs which showed only the ‘restless’ subscale did not significantly change following oral dexamphetamine challenge (Table 4.16.).

180 Table 4.16. – Repeated measures ANOVAs examining within-subject effects of Time (baseline, 60, 120, 180 and 270 mins following dexamphetamine challenge) and SAIRS subscale (‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’), and ANOVAs examining the within-subject effects of Time in each individual SAIRS subscale (‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’) in 20 healthy controls. Effects F-ratio (effect df, error df) p value Repeat Measures ANOVA examining all SAIRS subscales Time 51.5 (1.9, 36.7) <0.001 SAIRS subscale 7.8 (2.7, 50.6) <0.001 Within-Subject Effects Time x SAIRS 7.0 (4.8, 92.0) <0.001 subscale Repeat Measures ANOVA examining ‘EUPHORIC’ subscale Within-Subject Effects Time 5.1 (2.9, 54.4) 0.004 Repeat Measures ANOVA examining ‘ALERT’ subscale Within-Subject Effects Time 3.5 (2.9, 55.9) 0.021 Repeat Measures ANOVA examining ‘RESTLESS’ subscale Within-Subject Effects Time 0.8 (1.9, 36.4) 0.466 Repeat Measures ANOVA examining ‘ANXIOUS’ subscale Within-Subject Effects Time 5.1 (2.5, 46.9) 0.006

Post-hoc paired t-tests were used to examine the changes in ‘Euphoric’, ‘Alert’ and ‘Anxious’ scores at each time point compared with baseline scores. These showed significant increases in ‘Euphoric’ and decreases in ‘Anxious’ scores compared with baseline (Bonferroni corrected significance threshold p<0.0125). There were increases in ‘Alert’ scores that did not survive correction for multiple comparisons (Figure 4.9.).

181 SAIRS Euphoric SAIRS Alert

10 ** ** ** 10 * * * 8 8

6 6

4 4 SAIRS Alert Score

SAIRS Euphoria Score 2 Scan 2

0 0 0 60 120 180 240 300 0 60 120 180 240 300 Time (mins) Time (mins)

SAIRS Restless SAIRS Anxious

10 10

8 8

6 6 * ** 4 4 SAIRS Anxiou Score SAIRS Restless Score 2 2

0 0 0 60 120 180 240 300 0 60 120 180 240 300 Time (mins) Time (mins)

Figure 4.9. – Mean (±SD) SAIRS ‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’ scores in 20 healthy controls: pre- and post-dexamphetamine challenge (paired sample t-test compared with 0 mins *uncorrected p<0.05, **Bonferroni corrected significance threshold p<0.013).

4.3.12. Subjective effects of oral dexamphetamine challenge in alcohol dependent participants: SAIRS scores

SAIRS scale scores were available for 13 alcohol dependent participants. A repeated measures ANOVA examining the effect of the oral dexamphetamine challenge on subjective SAIRS ratings across all subscales in alcohol dependent participants showed a significant effect of Time but not SAIRS subscale and no significant SAIRS subscale x Time interaction (Table 4.17.). Exploratory repeat measures ANOVAs examining if there were any effects of dexamphetamine on individual SAIRS subscales did not show any significant results (Table 4.17. and Figure 4.10.).

182 Table 4.17. – Repeated measures ANOVA examining within-subject effects of Time (baseline, 60, 120, 180 and 270 mins following dexamphetamine challenge) and SAIRS subscale (‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’), and ANOVAs examining the within-subject effects of Time in each individual SAIRS subscale (‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’) in 13 alcohol dependent participants. Effects F-ratio (effect df, error df) p value Repeat Measures ANOVA examining all SAIRS subscales Time 18.8 (1.2, 14.6) <0.001 SAIRS subscale 2.4 (2.1, 25.1) 0.109 Within Subject effects Time x SAIRS 2.5 (3.5, 42.2) 0.061 subscale Repeat Measures ANOVA examining ‘EUPHORIC’ subscale Within Subject effects Time 1.2 (2.5, 29.6) 0.310 Repeat Measures ANOVA examining ‘ALERT subscale Within Subject effects Time 0.8 (2.5, 29.7) 0.482 Repeat Measures ANOVA examining ‘RESTLESS subscale Within Subject effects Time 0.5 (2.2, 26.0) 0.649 Repeat Measures ANOVA examining ‘ANXIOUS’ subscale Within Subject effects Time 1.1 (2.7, 32.2) 0.359

183 SAIRS Euphoric SAIRS Alert

10 10

8 8

6 6

4 4 SAIRS Alert Score

SAIRS Euphoria Score 2 Scan 2

0 0 0 60 120 180 240 300 0 60 120 180 240 300 Time (mins) Time (mins) SAIRS Restless SAIRS Anxious

10 10

8 8

6 6

4 4 SAIRS Anxious Score SAIRS Restless Score 2 2

0 0 0 60 120 180 240 300 0 60 120 180 240 300 Time (mins) Time (mins) Figure 4.10. – Mean (±SD) SAIRS ‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’ scores in 13 alcohol dependent participants: pre- and post-dexamphetamine challenge.

4.3.13. Comparing SAIRS scores between healthy controls and alcohol dependent participants

To compare SAIRS scores between healthy control and alcohol dependent participants a ‘change in SAIRS score from baseline’ value was calculated for each participant at each timepoint (e.g. change in SAIRS ‘Alert’ at 60 mins = SAIRS ‘Alert’ 60 mins – SAIRS ‘Alert’ baseline). The effect of dexamphetamine to change SAIRS scores is the measure of interest in this analysis therefore calculating a ‘change in SAIRS score from baseline’ value accounts for any differences in baseline scores between groups which are not of interest but may influence results. All results and data presented in this section are ‘Change in SIARS score’.

184 A mixed model ANOVA examining whether there is a difference in the effect of the oral dexamphetamine challenge on the change in scores across the SAIRS subscales compared between healthy controls and alcohol dependent participants showed a significant SAIRS subscale x Status interaction (Table 4.18.). This significant result was further examined within each individual SAIRS subscale where the mixed model ANOVAs showed significant within- subject effects of Time on change in SAIRS ‘Euphoric’ scores and significant between-subject effects of Status and Time x Status interaction on change in SAIRS ‘Anxious’ scores (Table 4.18.).

185 Table 4.18. – Mixed model ANOVA examining within-subject effects of Time (60, 120, 180 and 270 mins following dexamphetamine challenge) and SAIRS subscales (change from baseline – ‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’) and between subject effect of Status (healthy control and alcohol dependent) on SIARS scores in 20 healthy controls and 13 alcohol dependent participants. Also, ANOVAs examining the within-subject effect of Time and between-subject effect of Status in each individual SAIRS subscale (change from baseline – ‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’). Effects F-ratio (effect df, error df) p value Mixed Model ANOVA examining all SAIRS subscales (change in score from baseline) Time 3.4 (2.2, 67.8) 0.036 Time x Status 0.5 (2.2, 67.8) 0.615 SAIRS subscale 2.5 (2.4, 74.0) 0.081 SAIRS subscale x 4.7 (2.4, 74.0) 0.009 Within-subject effects Status Time x SAIRS subscale 1.2 (5.5, 170.7) 0.319 Time x SAIRS subscale 0.9 (5.5, 170.7) 0.518 x Status Between-subject effects Status 0.1 (1,31) 0.822 Mixed Model ANOVA examining ‘EUPHORIC’ subscale (change in score from baseline) Time 3.5 (2.4, 74.3) 0.028 Within-subject effects Time x Status 0.3 (2.4, 74.3) 0.771 Between-subject effects Status 3.4 (1, 31) 0.073 Mixed Model ANOVA examining ‘ALERT’ subscale (change in score from baseline) Time 2.7 (2.3, 72.7) 0.069 Within-subject effects Time x Status 0.5 (2.3, 72.7) 0.658 Between-subject effects Status 1.7 (1, 31) 0.207 Mixed Model ANOVA examining ‘RESTLESS’ subscale (change in score from baseline) Time 1.0 (2.2, 69.3) 0.389 Within-subject effects Time x Status 0.2 (2.2, 69.3) 0.844 Between-subject effects Status 0.4 (1, 31) 0.511 Mixed Model ANOVA examining ‘ANXIOUS’ subscale (change in score from baseline) Time 2.8 (2.4, 73.8) 0.057 Within-subject effects Time x Status 3.3 (2.4, 73.8) 0.035 Between-subject effects Status 4.6 (1, 31) 0.040

Post-hoc independent sample t-tests were used to examine differences in SAIRS ‘Euphoric’ and ‘Anxious’ (change in scores from baseline) between healthy controls and alcohol dependent participants. There were significantly lower changes in SAIRS ‘Anxiety’ scores in healthy controls at 270 mins post-dexamphetamine challenge (Bonferroni corrected significance threshold p<0.013) (Figure 4.11.). Higher change in SAIRS ‘Euphoric’ scores at 270 mins post-dexamphetamine and lower change in SAIRS ‘Anxiety’ scores at 180 mins post-

186 dexamphetamine in healthy controls did not survive correction for multiple comparisons (Bonferroni corrected significance threshold p<0.013) (Figure 4.11.).

Alcohol Dependent Healthy Control

SAIRS Euphoric SAIRS Alert

4 4 * 2 2

0 0 from baseline from baseline -2 -2 Scan Change in SAIRS Alert Change in SAIRS Euphoria -4 -4 0 60 120 180 240 300 0 60 120 180 240 300

Time (mins) Time (mins) SAIRS Restles SAIRS Anxious

4 4 * ** 2 2

0 0 from baseline from baseline -2 -2 Change in SAIRS Anxious Change in SAIRS Restless

-4 -4 0 60 120 180 240 300 0 60 120 180 240 300

Time (mins) Time (mins)

Figure 4.11. – Mean (±SD) change in SAIRS scale scores (‘Euphoric’, ‘Alert’, ‘Restless’ and ‘Anxious’) from baseline in 20 healthy controls and 13 alcohol dependent participants (**Bonferroni corrected significance threshold p<0.013, *uncorrected p<0.05).

4.3.14. Associations between change in SAIRS scores from baseline and

11 [ C]carfentanil ∆BPND

11 The association between NAcc [ C]carfentanil ∆BPND and change in SAIRS scores from baseline to 180 minutes post-dexamphetamine (i.e. start of post-dexamphetamine PET scan) were examined in healthy controls and alcohol dependent participants separately using

187 Pearson’s correlation coefficient. There was a correlation between higher NAcc

11 [ C]carfentanil ∆BPND and greater changes in SAIRS ‘Alert’ at 180 mins in healthy controls (R=0.459, p=0.042, Figure 4.12.) but this result did not survive Bonferroni correction for multiple comparisons (Bonferroni corrected significance threshold p<0.0125).

Figure 4.12. – Associations between NAcc 6 11 [ C]Carfentanil ∆BPND and change in SAIRS 4 ‘Alert’ scores 180 mins following 2 dexamphetamine challenge in 20 healthy controls (R=0.459, p=0.042).

(180mins) 0

Change in SAIRS Alert -2

-4 -0.05 0.00 0.05 0.10 0.15 0.20 ¹¹ NAcc [ C]carfentanil ∆BPND

11 4.3.15. Associations between [ C]Carfentanil ∆BPND and measures related to alcohol use and dependence: duration of abstinence, lifetime harmful alcohol exposure, SADQ and TRQ scores

11 Duration of abstinence (days) and NAcc [ C]Carfentanil ∆BPND were not significantly correlated (Spearman’s Rho=0.014, p=0.964). Further exploratory analysis examining if

11 duration of abstinence was correlated with [ C]Carfentanil ∆BPND in the remaining 20 ROIs also did not show any significant correlations (all uncorrected p>0.05).

Harmful alcohol exposure (total lifetime weeks with mean >60g alcohol consumption per day)

11 and NAcc [ C]Carfentanil ∆BPND were not significantly correlated (Pearson’s R=-0.106, p=0.731). Further exploratory analysis examining if alcohol exposure was correlated with

11 [ C]Carfentanil ∆BPND in the remaining 20 ROIs also did not show any significant correlations (all uncorrected p>0.05).

188 11 SADQ scores and NAcc [ C]Carfentanil ∆BPND were not significantly correlated (Pearson’s R=- 0.050, p=0.872). Exploratory analysis did not show any significant correlations between SADQ

11 scores and [ C]Carfentanil ∆BPND in the remaining 20 ROIs (all uncorrected p>0.05).

11 TRQ scores and NAcc [ C]Carfentanil ∆BPND were not significant correlated (Pearson’s R=0.302, p=0.317). Exploratory analysis did not show any significant correlations between

11 TRQ scores and [ C]Carfentanil ∆BPND in the remaining 20 ROIs (all uncorrected p>0.05).

11 4.3.16. Associations between [ C]carfentanil ∆BPND and other clinical variables: UPPS-P impulsivity, BDI, SSAI and STAI scores

UPPS-P impulsivity scale subscales Positive Urgency and Negative Urgency were significantly different between alcohol dependent participants and healthy controls. There were no significant correlations between Positive or Negative Urgency scores and NAcc

11 [ C]carfentanil ∆BPND in healthy controls (Pearson’s R=0.127, p=0.664 and R=0.173, p=0.555 respectively) or alcohol dependent participants (Pearson’s R=0.173, p=0.555 and R=0.547, p=0.066 respectively). Exploratory analyses examining correlations between UPPS-P Negative

11 Urgency and Positive Urgency scores and [ C]carfentanil ∆BPND in the remaining 20 ROIs also did not show any significant correlations (uncorrected p>0.05) in healthy controls or alcohol dependent participants.

11 BDI scores and NAcc [ C]carfentanil ∆BPND were not significantly correlated in healthy controls (Spearman’s Rho=-0.268, p=0.253) or alcohol dependent participants (Spearman’s Rho=-0.444, p=0.128). Further exploratory analyses examined the associations between BDI

11 scores and [ C]carfentanil ∆BPND in the remaining 20 ROIs. In alcohol dependent participants

11 there was an association with amygdala [ C]carfentanil ∆BPND (Spearman’s R=-0.671, p=0.012, Figure 4.13) which did not survive correction for multiple comparisons (Bonferroni corrected significance threshold p<0.0024). There were no other significant correlations in either healthy controls or alcohol dependent participants (all uncorrected p>0.05).

189 Figure 4.13. – Associations between 0.20 ND 11 0.15 amygdala [ C]carfentanil ∆BPND and 0.10 BDI scores in 13 alcohol dependent 0.05 participants (Spearman’s R=-0.671, p=0.012).

C]carfentanil ∆BP 0.00 ¹¹ -0.05 -0.10

Amygdala [ -0.15 0 2 4 6 8 10 12 BDI score

11 STAI scores and NAcc [ C]carfentanil ∆BPND were not significantly correlated in healthy controls (Pearson’s R=-0.291, p=0.227) or alcohol dependent participants (Pearson’s R=- 0.282, p=0.351). Exploratory analyses examining correlations between STAI scores and

11 [ C]carfentanil ∆BPND in the remaining 20 ROIs also did not show any significant correlations (all uncorrected p>0.05) in healthy controls or alcohol dependent participants.

SSAI scores collected prior to the post-dexamphetamine [11C]carfentanil PET scan and NAcc

11 [ C]carfentanil ∆BPND were not significantly correlated in healthy controls (Pearson’s R=0.028, p=0.908) or alcohol dependent participants (Pearson’s R=0.231, p=0.448). Exploratory analyses did not show any significant (all uncorrected p>0.05) correlations

11 between SSAI scores and [ C]carfentanil ∆BPND in any of the remaining 20 ROIs in healthy controls or alcohol dependent participants. Furthermore, repeat measures ANOVAs did not show any significant effect of Time on SSAI scores following the oral dexamphetamine dose in healthy controls or alcohol dependent participants.

4.3.17. Examining the potential confounding effect of current smoking and nicotine

11 dependence on [ C]carfentanil ∆BPND

In healthy controls a mixed model ANOVA examining the effect of current smoking status on

11 [ C]carfentanil ∆BPND did not show any significant between-subject effect of Smoking (Table 4.19.).

190 Table 4.19. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between-

11 subject effect of Smoking (current smokers or current non-smokers) on [ C]carfentanil ∆BPND in 3 current smoker and 17 current non-smoker healthy controls. Effects F-ratio (effect df, error df) p value ROI 0.8 (4.9, 88.5) 0.564 Within-subject factors ROI x Smoking 0.3 (4.9, 88.5) 0.906 Between-subject factors Smoking 0.0 (1, 18) 0.967

Exploratory independent sample t-tests did not show any significant differences in

11 [ C]carfentanil ∆BPND in any ROI compared between current smoker and current non-smoker healthy controls (all uncorrected p<0.05). As shown in Figure 4.14. the current smoker healthy

11 controls appear to have [ C]carfentanil ∆BPND within a similar range to current non-smoker healthy controls.

In alcohol dependent participants a mixed model ANOVA examining the effect of current

11 smoking status on [ C]carfentanil ∆BPND did not show any significant between-subject effect of Smoking (Table 4.20.). However, there was a significant ROI x Smoking interaction.

Table 4.20. – Mixed model ANOVA examining the within-subject effect of ROI (21 regions) and between-

11 subject effect of Smoking (current smokers or current non-smokers) on [ C]carfentanil ∆BPND in 7 current smoker and 6 current non-smoker alcohol dependent participants. Effects F-ratio (effect df, error df) p value ROI 1.8 (4.9, 54.4) 0.13 Within-subject factors ROI x Smoking 3.0 (4.9, 54.4) 0.019 Between-subject factors Smoking 0.5 (1, 11) 0.509

This significant ROI x Smoking interaction was investigated using independent sample t-tests

11 to examine differences in [ C]carfentanil ∆BPND compared between current smoker and current non-smoker alcohol dependent participants. None of these t-tests were significant following correction for multiple comparisons (Bonferroni corrected significance threshold p<0.0125). Similarly to healthy controls, alcohol dependent current smokers had NAcc

11 [ C]carfentanil ∆BPND values within a similar range to current non- smokers (Figure 4.14.).

191 11 Healthy Controls Figure 4.14. – NAcc [ C]carfentanil

Alcohol Dependent ∆BPND (including mean value) in current 0.2

ND non-smoking healthy controls (n=17) 0.1 and alcohol dependent participants (n=6) and current smoking controls 0.0 (n=3) and alcohol dependent C]carfentanil ∆BP ¹¹ -0.1 participants (n=7).

NAcc [ -0.2 Current Current Smokers Non-smokers

To examine if current smoking status had any mediating effect on the differences in

11 [ C]carfentanil ∆BPND observed between healthy controls and alcohol dependent participants the mixed model ANOVA from Section 4.3.4. was rerun with current smoking status as a covariate. This showed no significant between-subject effect of Smoking and no significant Smoking x Status interaction. The previous between-subject effect of Status remained significant with smoking status as a covariate (Table 4.21.).

Table 4.21. – Mixed Model ANOVA examining the within-subject effect of ROI (21 regions) and between- subject effects of Smoking (current smokers or non-current smokers) and Status (healthy control or alcohol

11 dependent participant) on [ C]carfentanil ∆BPND in 20 healthy controls and 13 alcohol dependent participants. Effects F-ratio (effect df, error df) p value ROI 1.8 (6.2, 178.7) 0.095 ROI x Smoking 1.3 (6.2, 178.7) 0.25 Within-subject factors ROI x Status 0.6 (6.2, 178.7) 0.751 ROI x Smoking 1.5 (6.2, 178.7) 0.164 x Status Smoking 0.4 (1, 29) 0.509 Status 14.7 (1, 29) 0.001 Between-subject factors Smoking x 0.4 (1, 29) 0.534 Staus

11 FTND scores and NAcc [ C]carfentanil ∆BPND were not significantly correlated in current smoker healthy controls (Pearson’s R=0.111, p=0.929) or alcohol dependent participants (Pearson’s R=-0.101, p=0.829). Exploratory Pearson’s correlation coefficient analyses

192 11 examining associations between FTND scores and [ C]carfentanil ∆BPND in the remaining 20 ROIs also showed no significant correlations (all uncorrected p>0.05).

4.3.18. Examining the potential confounding effects of the OPRM1 A118G

11 polymorphism on [ C]carfentanil ∆BPND

OPRM1 A118G polymorphism data were available for 14 healthy controls and 13 alcohol

11 dependent participants. The differences in [ C]carfentanil ∆BPND between OPRM1 G-allele carriers (G:G or G:A) compared with homozygous A:A individuals were examined in healthy controls and alcohol dependent participants separately in all 21 ROIs. A mixed model ANOVA did not show any significant between-subject effect of Genotype or Genotype x ROI interaction in either healthy controls or alcohol dependent participants (Table 4.22.).

Table 4.22. – Mixed model ANOVA examining within-subject effects of ROI (21 regions) and between

11 subject effect of Genotype (OPRM1 A:A or G:G/G:A) on [ C]carfentanil ∆BPND in 14 healthy controls and 13 alcohol dependent participants. F-ratio (effect df, Group Effects p value error df) Within-subject ROI 1.2 (4.0, 47.5) 0.337 factors ROI x Genotype 1.8 (4.0, 47.5) 0.155 Healthy controls Between-subject Genotype 0.4 (1, 12) 0.533 factors

Within-subject ROI 1.2 (4.6, 50.4) 0.322 Alcohol factors ROI x Genotype 0.8 (4.6, 50.4) 0.556 dependent Between-subject participants Genotype 0.9 (1, 11) 0.371 factors

Exploratory post-hoc independent sample t-tests were carried out to compare

11 [ C]carfentanil ∆BPND in all 21 ROIs between OPRM1 G-allele carriers (G:G or G:A) and homozygous A:A individuals in healthy controls and alcohol dependent participants separately (Figures 4.15. and 4.16.). There were no significant differences in [11C]carfentanil

∆BPND between G-allele carriers (G:G or G:A) and homozygous A:A in any ROI in healthy controls or alcohol dependent participants following correction for multiple comparisons (Bonferroni corrected significance threshold p<0.0023 or P-plot Hochberg correction).

193

Ventral pallidum

Thalamus Healthy Control A:A Homozygous Putamen Healthy Control G-allele (G:G or A:G) Orbitofrontal

Insula

Hypothalamus

Cerebellum

Caudate

Anterior cingulate

Amygdala

NAcc

0.00 0.04 0.08 0.12 0.16 0.20 [¹¹C]carfentanil ∆BP ND 11 Figure 4.15. – Mean (±SD) [ C]carfentanil ∆BPND in healthy control OPRM1 A-allele homozygous (n=10) and G-allele carrier (G:G/G:A, n=4) participants.

194 Ventral pallidum

Thalamus Alcohol Dependent A:A Homozygous Putamen Alcohol Dependent G-allele (G:G or A:G) Orbitofrontal

Insula

Hypothalamus

Cerebellum

Caudate

Anterior cingulate

Amygdala

NAcc

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 [¹¹C]carfentanil ∆BP ND 11 Figure 4.16. – Mean (±SD) [ C]carfentanil ∆BPND in alcohol dependent OPRM1 A-allele homozygous (n=9) and G-allele carrier (G:G/G:A, n=4) participants.

4.4. Discussion

In this chapter we have shown that in healthy controls there is measurable endogenous opioid release in 21 high MOR availability ROIs using the oral 0.5mg/kg dexamphetamine challenge and [11C]carfentanil PET protocol. In comparison, abstinent alcohol dependent participants did not show any significant endogenous opioid release following the oral 0.5mg/kg dexamphetamine challenge. In alcohol dependent participants oral dexamphetamine- induced endogenous opioid release was significantly blunted compared with healthy controls in 17 out of 21 ROIs examined. There was no significant difference in the blunted oral

195 dexamphetamine-induced endogenous opioid release in gambling disorder compared with alcohol dependence.

Blunted oral dexamphetamine-induced endogenous opioid release was not associated with blunted positive subjective effects (‘euphoria’) of dexamphetamine in alcohol dependent participants. Furthermore, there was no evidence of significant associations between blunted endogenous opioid release and clinical factors, including duration of abstinence and severity of dependence in abstinent alcohol dependent participants.

4.4.1. Oral dexamphetamine-induced endogenous opioid release in healthy controls

11 There was a significant reduction in [ C]carfentanil BPND following oral 0.5mg/kg dexamphetamine challenge across all 21 high [11C]carfentanil binding regions investigated in healthy controls. Previous published studies using smaller subsamples of the population used in this thesis showed significant endogenous opioid release following oral dexamphetamine challenge in some regions, but in other regions such as the amygdala and hypothalamus

11 reductions in [ C]carfentanil BPND were not significant (Colasanti et al, 2012; Mick et al, 2014, 2016). Our results suggest that these previous studies may have been underpowered to

11 detect changes in [ C]carfentanil BPND in some regions. Several regions with high MOR availability that were not previously examined in the prior studies, for example globus pallidus, hippocampus, temporal lobe and ventral pallidum, have also shown significant

11 reductions in [ C]carfentanil BPND following oral dexamphetamine challenge. This suggests that there is a widespread release of endogenous opioids following oral dexamphetamine challenge across MOR rich regions.

β-endorphin is an endogenous opioid peptide with high MOR affinity and is produced by cleavage of pro-opiomelanocortin (POMC) in a number of brain regions including the NAcc, thalamus, amygdala and hypothalamus (Cone, 2005; Le Merrer et al, 2009). The central nervous system POMC neurons originate in the arcuate nucleus of the hypothalamus and project to these brain regions (Cone, 2005), and one potential mechanism for dexamphetamine-induced endogenous opioid release is an activation of hypothalamic POMC

196 neurons leading to increased β-endorphin production. However, we have also observed

11 reductions in [ C]carfentanil BPND in regions such as the caudate and putamen where there is less evidence of the presence of POMC or β-endorphin (Gramsch et al, 1979; Le Merrer et al, 2009). In these regions other endogenous opioid peptides, for example enkephalins and endomorphins, bind to MOR and these peptides may be responsible for the displacement of

11 [ C]carfentanil BPND following oral dexamphetamine challenge in these regions (Assis et al, 2006; Banghart et al, 2015; Gramsch et al, 1979; Quelch et al, 2014; Wang and McGinty, 1995).

The mechanism by which oral dexamphetamine induces a release of endogenous opioids is poorly understood. Whilst intravenous dexamphetamine produces strong euphoric subjective effects which are blunted by opioid receptor blockade with naltrexone (Jayaram-

11 Lindström et al, 2004), there are no changes in [ C]carfentanil BPND immediately following intravenous dexamphetamine administration (Guterstam et al, 2013). There is evidence from animal research that a period of 90 minutes following a dexamphetamine dose in rats may be required to reach peak NAcc β-endorphin concentrations and measurable reductions in MOR availability (Olive et al, 2001; Quelch et al, 2014). This is in keeping with the three hour period between oral 0.5mg/kg dexamphetamine administration and [11C]carfentanil PET scanning to obtain a measurable endogenous opioid release in humans (Colasanti et al, 2012; Mick et al, 2014, 2016). Dexamphetamine increases extracellular concentrations of the monoamines dopamine, noradrenaline and 5-HT (serotonin) (Rothman et al, 2001), and it is likely that the mechanism for dexamphetamine-induced opioid release is mediated through these changes in monoamines (Colasanti et al, 2012). There is evidence that D2 blockade reduces cocaine- induced endogenous opioid release in the NAcc (Doron et al, 2006; Soderman and Unterwald, 2009), although 5-HT and noradrenaline may also play an important role in mediating endogenous opioid release in brain regions with lower dopamine transporter (DAT) density, such as the frontal cortex and thalamus (Ciliax et al, 1999; Colasanti et al, 2012). Furthermore, whilst dexamphetamine induces an immediate increase in extracellular dopamine, as measured with [11C]raclopride PET (Breier et al, 1997; Martinez et al, 2003, 2005), a period of time is required for a change in endogenous opioid concentrations to be measurable with

11 [ C]carfentanil BPND PET (Colasanti et al, 2012; Guterstam et al, 2013; Mick et al, 2014, 2016). This delay may suggest an indirect mechanism of dexamphetamine-induced increases in

197 monoamines stimulating the release of endogenous opioid, but further research is required to understand this.

4.4.2. Blunted dexamphetamine induced-endogenous opioid release in abstinent alcohol dependent participants

11 In contrast to healthy controls, there were no significant changes in [ C]carfentanil BPND following oral dexamphetamine challenge in any of the 21 ROIs in alcohol dependent

11 participants. When directly comparing [ C]carfentanil ∆BPND values between healthy controls and alcohol dependent participants across the 21 ROIs there was a blunted endogenous opioid release in 17 regions (excluding amygdala, caudate, hippocampus and hypothalamus). These findings are similar to previous findings of blunted endogenous opioid release in response to an oral dexamphetamine challenge in gambling disorder (Mick et al, 2016), and suggests that dysregulation of endogenous opioid tone may underpin both behavioural and substance addictions.

One potential mechanism for the blunted oral dexamphetamine-induced endogenous opioid release in alcohol dependence may be a blunted monoamine response. There is evidence of lower dexamphetamine-induced dopamine release in alcohol dependent individuals during early abstinence in both striatal and cortical areas (Martinez et al, 2005; Narendran et al, 2014). The blunted striatal dopamine release was shown using 0.3mg/kg intravenous dexamphetamine and [11C]raclopride PET (Martinez et al, 2005). However, given the differences between oral and intravenous dexamphetamine-induced endogenous opioid release discussed above (Colasanti et al, 2012; Guterstam et al, 2013; Mick et al, 2014), it is difficult to draw comparisons between intravenous dexamphetamine-induced dopamine responses and oral dexamphetamine-induced endogenous opioid responses. The findings of blunted cortical dopamine release in cortical regions by Narendran et al. (Narendran et al, 2014), are more relevant as the [11C]FLB 457 PET scan was carried out three hours following a 0.5 mg/kg oral dexamphetamine challenge, which is similar to our [11C]carfentanil protocol.

198 Both studies showing blunted dexamphetamine-induced dopamine release in alcohol dependence were carried out in early abstinence: 14 days abstinence in the Martinez et al. (2005) study and mean 33 days abstinence in the Narendran et al. study. There is

18 [ F]raclopride PET evidence that the low D2/3 receptor availability in alcohol dependent individuals ‘normalises’ (e.g. becomes higher) during the 1st year of abstinence from alcohol (Rominger et al, 2012). Therefore, it is possible that dopaminergic signalling may ‘recover’ and may not be blunted with the longer durations of abstinence in our alcohol dependent participants.

Furthermore, in gambling disorder there is evidence of higher dopamine release following an oral dexamphetamine challenge ([11C]-(+)-PHNO PET 2-hours following 0.4 mg/kg dexamphetamine) (Boileau et al, 2014). However, similarly to alcohol dependence there is also blunted oral dexamphetamine-induced endogenous opioid release (Mick et al, 2016). If there is a shared mechanism of blunted dexamphetamine endogenous opioid release in both alcohol dependence and gambling disorder, it is unlikely to be due to blunted dexamphetamine-induced dopamine release.

As discussed above in Section 4.4.1, the mechanism by which dexamphetamine induces endogenous opioid release is likely to be downstream of increases in extracellular dopamine and other monoamines. The blunted dexamphetamine-induced endogenous opioid release in alcohol dependence and gambling disorder may also be due to mechanisms downstream from the changes in monoamine concentrations.

4.4.3. Dysregulated salience responses in addiction as a mechanism for blunted oral dexamphetamine-induced endogenous opioid release

There is evidence that in addicted individuals there are higher responses to addiction- associated cues and blunted responses to non-addiction related cues or rewards (Koob and Volkow, 2016; Lubman et al, 2008). For example, alcohol dependent individuals have blunted fMRI BOLD responses to non-salient financial rewards (Beck et al, 2009; Nestor et al, 2017; Wrase et al, 2007) and higher responses to salient alcohol cues (Schacht et al, 2013a). Our

199 alcohol dependent participants have no previous history of dependence to any substance except alcohol and therefore dexamphetamine may be non-salient in these participants leading to a blunted response. One potential study to investigate the effect of the salience of a reward on endogenous opioid release in alcohol dependence would be the administration of an alcohol challenge. There is evidence of endogenous opioid release following an oral alcohol challenge (Mitchell et al, 2012), and it might be expected that this endogenous opioid release would be enhanced in alcohol dependent individuals as alcohol is a salient reward. However, such an experiment in abstinent alcohol dependent participants would not be ethical.

There is also evidence of higher fMRI brain responses to addiction related cues in gambling disorder (Limbrick-Oldfield et al, 2017), and dysregulation of salient responses may be a common mechanism for the blunted oral dexamphetamine-induced endogenous opioid release in both alcohol dependence and gambling disorder. The impact of salience on endogenous opioid release and financial reward responses will be explored and discussed further in Chapter 5.

11 4.4.4. Comparison of [ C]carfentanil ∆BPND between alcohol dependence and gambling disorder

There were no significant differences in the endogenous opioid response to oral dexamphetamine between alcohol dependent and gambling disorder participants. This suggests that heavy alcohol use may not have an additional ‘toxic’ effect on blunting endogenous opioid tone in alcohol dependence compared with gambling disorder. Furthermore, as discussed above the lack of difference in the degree of blunting of dexamphetamine-induced endogenous opioid release in alcohol dependence compared with gambling disorder may indicate a similar mechanism for this blunted endogenous opioid release between these substance and behavioural addictions.

200 4.4.5. Plasma dexamphetamine concentrations in healthy controls and alcohol dependent participants

Examining plasma dexamphetamine concentrations in both healthy controls and alcohol dependent participants suggests that maximum concentrations were reached by the start of the post-dexamphetamine [11C]carfentanil PET scan, and that the plasma concentrations remained relatively stable for the duration of the 90 minute scan. Plasma dexamphetamine levels appeared to be lower in alcohol dependent participants compared with healthy controls, particularly at 120 and 270 mins post-dexamphetamine challenge.

Lower dexamphetamine concentrations in alcohol dependent participants could be due to faster metabolism in alcohol dependent participants. Dexamphetamine is metabolised by the cytochrome P450 (CYP) pathway (Caldwell, 1976) and there is evidence of CYP induction, including CYP2D6 and CYP2E1, in current drinking alcohol dependent individuals (Dupont et al, 1998; Miksys et al, 2002). It is possible that induced CYP pathway metabolism in our alcohol dependent participants resulted in faster metabolism of dexamphetamine, and therefore lower dexamphetamine plasma concentrations. However, it is unclear if this CYP induction persists into long-term abstinence. One study in non-dependent drinkers found the CYP2E1 induction after 40 g daily alcohol consumption for 4 weeks disappeared within 8 days of stopping alcohol consumption (Oneta et al, 2002). Furthermore, Matinez et al. (2005) did not find lower plasma dexamphetamine concentrations in alcohol dependent participants following intravenous administration. This may suggest that differences in absorption following oral administration rather than metabolism may be a cause for the trend of lower dexamphetamine concentrations in our alcohol dependent participants. Chronic alcohol use has been shown to reduce absorption in the gut of a range of nutrients including proteins and fats (Bode and Bode, 2003), however the effects of chronic alcohol use on drug absorption, particularly after long periods of abstinence is not well characterised. The GC-MS method using in our dataset gives total plasma dexamphetamine concentrations and therefore differences in the proportion of plasma protein bound dexamphetamine would not alter our plasma dexamphetamine concentrations measurement.

201 It is possible that lower plasma dexamphetamine concentration in alcohol dependent participants may be the cause of the blunted endogenous opioid release. To explore this we examined associations between 180 minutes post-dexamphetamine plasma

11 dexamphetamine concentrations and [ C]carfentanil ∆BPND. In addition to examining correlations with plasma dexamphetamine concentrations at 180 minutes post- dexamphetamine challenge, plasma dexamphetamine AUC concentrations were also calculated for correlational analyses. Two different plasma dexamphetamine AUC values were calculated, the first represented the duration of the PET scan (180 to 270 mins) to reflect

11 that the post-dexamphetamine [ C]carfentanil BPND values used data collected across the entire duration of the 180 minute PET scan and therefore differences in plasma dexamphetamine during the scan may have an impact on endogenous opioid release and MOR availability during the scan. Dexamphetamine does not cause an immediate measurable release of endogenous opioids, but requires a period of up to 3 hours for endogenous opioid release to be detected (see Section 4.4.1. for details). Therefore, a second plasma dexamphetamine AUC (0 to 270 mins) was calculated in an attempt to reflect any differences in plasma dexamphetamine concentrations during the entire period prior to and during the post-dexamphetamine [11C]carfentanil PET scan which may affect endogenous opioid release and post-dexamphetamine MOR availability.

11 There were no significant correlations between [ C]carfentanil ∆BPND and plasma dexamphetamine concentrations 180 mins post-dexamphetamine challenge, or either of our two plasma dexamphetamine AUC measures. This may suggest that within the range of plasma dexamphetamine concentrations in our participants, differences in plasma dexamphetamine concentrations may have little or no impact on endogenous opioid release. It is possible that the mechanism by which dexamphetamine induces a release of endogenous opioids is saturated at lower plasma dexamphetamine concentrations than those reached following our 0.5 mg/kg dose. However, we cannot exclude that lower dexamphetamine concentrations in alcohol dependent participants may be contributing to blunted endogenous opioid release. Further research measuring endogenous opioid release across a range of oral dexamphetamine doses may help to understand the magnitude of the impact that these lower dexamphetamine plasma concentrations are likely to have on endogenous opioid release.

202 4.4.6. Serum cortisol concentrations in healthy controls and alcohol dependent participants

There was a significant increase in serum cortisol concentrations following the oral dexamphetamine challenge in both our alcohol dependent participants and healthy controls. Dexamphetamine induced cortisol release is well document in the published literature, although the mechanisms are not well understood (Besser et al, 1969; Booij et al, 2016; Childs et al, 2016; Zack et al, 2015). There were no significant differences in serum cortisol concentrations between healthy controls and alcohol dependent participants. However, there appeared to be a differential response in alcohol dependent participants where individuals had ‘high’ or delayed or ‘low’ cortisol responses. The ‘high’ cortisol responders’ serum cortisol concentrations were similar to healthy volunteers’ responses, whilst the ‘low’ cortisol responders had significantly lower cortisol concentrations following the oral dexamphetamine challenge.

It should be noted, however, that the serum cortisol concentration value used to divide alcohol dependent participants into ‘high’ and ‘low’ responders (above or below 200ng/ml at 90 mins post-dexamphetamine challenge) was not based on any previous literature or specific hypothesis. Furthermore, due to the very small number of healthy control participants it is difficult to interpret the results comparing the cortisol responses of these participants with ‘high’ and ‘low’ responding alcohol dependent participants.

There is evidence that MORs inhibit the release of adrenocorticotropic hormone (ACTH), likely by modulating corticotrophin-releasing hormone (CRH) release in the hypothalamus, and blockade of MOR with naltrexone increases cortisol concentrations (see Figure 4.17.) (Stephens and Wand, 2012; Wand et al, 2011, 2012). Furthermore, higher MOR availability is associated with lower naltrexone-induced cortisol release (Wand et al, 2011, 2012). Therefore, we examined if differences in dexamphetamine induced endogenous opioid release might be mediating differences in cortisol responses. However, we did not find

11 evidence of differences in [ C]carfentanil ∆BPND in any ROIs compared between ‘high’ and ‘low’ cortisol responding alcohol dependent participants.

203

Noradrenaline GABA Figure 4.17. – A representation of the + - hypothalamic-pituitary–adrenal (HPA) MOR 5HT + - axis: indicating the pathway from CRF - release in the hypothalamus to cortisol HYPOTHALAMUS CRF producing neuron release from the adrenal gland and modulation of the HPA axis by MOR, GABA CRF + and monoaminergic signalling (adapted POMC producing cell PITUITARY GLAND from Stephens and Wand, 2012) -

+ ACTH

ADRENAL GLAND Cor3sol producing cell

CORTISOL

There is evidence that the associations between MOR availability and naltrexone-induced cortisol release are dysregulated in alcohol dependence (Wand et al, 2011, 2012), which could suggest that the endogenous opioid modulation of ACTH release is disrupted in alcohol dependence. There are monoamine neurotransmitters which also modulate cortisol responses, including dopamine, 5-HT and noradrenaline (see Figure 4.17.) (Jørgensen et al, 2002; Oswald et al, 2005; Stephens and Wand, 2012; Whitnall, 1993). For example, higher dopamine release in the striatum following intravenous 0.3mg/kg dexamphetamine is associated with higher cortisol release in healthy controls (Oswald et al, 2005) and both 5- HT2B antagonist methysergide or the destruction of serotonergic neurons with para- chloroamphetamine blunt cortisol responses to dexamphetamine challenge in rats (Knych and Eisenberg, 1979). It is possible that differences in monoamine neurotransmission, for example blunted dopaminergic responses to dexamphetamine shown in alcohol dependence (Martinez et al, 2005), may be mediating altered cortisol responses in alcohol dependence.

Given the evidence in alcohol dependence that cortisol and CRH may play an important role in relapse (Heilig and Koob, 2007; Lovallo, 2006), it would be of interest to further investigate the possible variability in cortisol responses to dexamphetamine in our data. Particularly to

204 examine if there are any clinical variables, for example relapse, which might be related with blunted cortisol responses in alcohol dependence.

4.4.7. Measuring subjective responses to the oral dexamphetamine challenge with SAIRS scores

There were significant changes in SAIRS ‘Euphoric’, ‘Alert’ and ‘Anxious’ subscales following dexamphetamine challenge in healthy controls but there were no significant changes in any SAIRS item in alcohol dependent participants. This may suggest that SAIRS ‘Euphoric’ subjective effects are blunted in alcohol dependent participants, although ‘Euphoric’ scores were not significantly difference in in alcohol dependent participants compared with controls. Compared with Mick et al. (2016), who found significantly lower SAIRS ‘Euphoric’ scores in gambling disorder participants compared with healthy controls, it is more difficult to interpret our results as either blunted or non-blunted subjective effects of dexamphetamine in our alcohol dependent participants. Further research, adequately powered to examine the subjective effects of dexamphetamine, is required to understand if alcohol dependent participants experience blunted subjective effects to an oral dexamphetamine challenge or not.

We also did not observe any associations between endogenous opioid release and SAIRS ‘Euphoric’ responses. Colasanti et al. (2012) found that higher ventral striatum (NAcc) endogenous opioid release was associated with higher SAIRS ‘Euphoria’ scores in a subset of six of our healthy control population, but this was not replicated by Mick et al. (2014) in a different subset of nine of our healthy controls or in the current larger combined sample with an additional five healthy controls (total n=20) examined in this thesis. Overall, the changes in SAIRS scores following the 0.5mg/kg oral dexamphetamine challenge were modest (Colasanti et al, 2012; Mick et al, 2014, 2016). One reason for using this dexamphetamine dose was to minimise the euphoric or other subjective effects which is of particular importance in individuals with a history of addiction (Colasanti et al, 2012; Mick et al, 2016).

205 Intravenous dexamphetamine (0.3 mg/kg) does produce marked and immediate subjective effects, but does not lead to measurable endogenous opioid release (Guterstam et al, 2013; Jayaram-Lindström et al, 2017). These subjective effects can be blunted with non-specific opioid receptor antagonist naltrexone (Jayaram-Lindström et al, 2004, 2017) suggesting that endogenous opioid signalling does play an important role in the subjective effects of dexamphetamine. It may be that opioid receptors other than MORs play a more important role in these dexamphetamine-induced subjective effects. For example, the delta opioid receptor also modulates rewarding hedonic responses (Castro and Berridge, 2014), but endogenous opioid binding to the delta receptor cannot be examined with the MOR specific [11C]carfentanil.

4.4.8. Associations between endogenous opioid release, duration of abstinence and harmful alcohol consumption

It was hypothesised in Chapter 3, Section 3.4.1. that there may be dynamic changes in endogenous opioid tone during abstinence which may explain why our alcohol dependent participants with longer durations of abstinence do not have higher MOR availability compared with healthy controls (see Section 3.3.2.). However blunted endogenous opioid release in our alcohol dependent participants appears to persist even into long durations of abstinence suggesting that low endogenous opioid tone does not recover with abstinence as suggested by Hermann et al. (2017).

There was also no association between heavy alcohol consumption and blunted endogenous

11 opioid release, which along with our lack of differences in [ C]carfentanil ∆BPND compared between alcohol dependent and gambling disorder participants, suggests that a toxic or pharmacological effect of heavy dependent alcohol consumption may not be the cause of the blunted dexamphetamine endogenous opioid release we observe.

One possibility is that blunted endogenous opioid release is a pre-existing risk factor for the development of alcohol dependence. This is in keeping with evidence showing lower plasma β-endorphin in individuals with a family history of alcohol dependence (Dai et al, 2005).

206

Another possibility is that the dysregulated opioidergic tone in our alcohol dependent participants is a result of ‘addiction’, rather than of alcohol use. This is supported by our finding of no difference in the degree of blunted oral dexamphetamine-induced endogenous opioid release between alcohol dependence and gambling disorder and may be a result of dysregulated salience responses (as discussed in Section 4.4.3.) or another mechanism. This dysregulated endogenous opioid tone which persists into abstinence may reflect a continued vulnerability factor for relapse to drinking.

4.4.9. Associations between endogenous opioid release, SADQ and TRQ scores

We hypothesised that higher severity of alcohol dependence (SADQ scores) and increased risk of relapse (TRQ scores) would be associated with more blunted endogenous opioid

11 release, but we did not find any associations [ C]carfentanil ∆BPND and SADQ or TRQ scores.

As previously discussed in Chapter 3, Section 3.4.2., the SADQ items primarily focus on physical withdrawal symptoms (Stockwell et al, 1979) and this may be less relevant to endogenous opioid signalling than other measures of dependence severity such as craving or loss of control. In gambling disorder there was also no association between blunted endogenous opioid release and severity of dependence (Mick et al, 2016), but no other published studies have examined endogenous opioid release in alcohol dependence.

The TRQ was developed using alcohol dependent individuals in active treatment, and may be less relevant in our long-term and stable abstinent alcohol dependent participants (Adinoff et al, 2010). It is possible that a different measurement of relapse risk, for example follow-up to record participants who did relapse, might provide a better approximation of relapse risk than TRQ scores in our participants.

207 4.4.10. Associations between BDI scores and oral dexamphetamine-induced endogenous opioid release

In alcohol dependent participants an exploratory analysis showed a trend of higher depressive symptoms (BDI scores) associated with lower amygdala oral dexamphetamine- induced endogenous opioid release. Previously published research in individuals with current major depressive disorder has used behavioural tasks, including social acceptance, social rejection and a sustained sadness task to induce endogenous opioid release (Hsu et al, 2015; Kennedy et al, 2006). These studies show either higher or lower endogenous opioid release in participants with major depression, depending on which behavioural task is used (Hsu et al, 2015; Kennedy et al, 2006). Due to the differences in challenge used to induce endogenous opioid release, for example a behavioural sadness task compared with oral dexamphetamine challenge, it is not possible to compare our results with these studies. Furthermore, our participants did not have a current diagnosis of depression.

The evidence of dysregulated endogenous opioid signalling in depression (Hsu et al, 2015; Kennedy et al, 2006) may be related to the broader dysregulation of reward responses shown in individuals with major depressive disorder (Whitton et al, 2015). There is also evidence of blunted reward responses in individuals with a previous history of depression who are now in remission (Pechtel et al, 2013). Therefore, it is possible that dysregulated endogenous opioid signalling is also present in individuals with major depression who are currently in remission, although there have been no published studies examining this. A persisting effect of depression on endogenous opioid signalling may be confounding factor when examining dexamphetamine induced endogenous opioid release in our alcohol dependent and gambling disorder participants, many of whom have a previous history of depression, compared with health controls who have no previous depression history. This issue could be further examined by investigating if dexamphetamine induced endogenous opioid release is blunted in a group of individuals with a previous depression diagnosis and currently in remission. Further discussion about depression in our participants and the confounding effect this may have on our results can be found in Section 5.4.9..

208 4.4.11. Associations between UPPS-P impulsivity scale scores and oral dexamphetamine-induced endogenous opioid release

We did not observe any significant associations between dexamphetamine-induced endogenous opioid release and UPPS-P Positive Urgency or Negative Urgency subscale scores in either healthy controls or alcohol dependent participants.

One previously published study has shown higher impulsivity associated with greater stress- induced endogenous opioid release in healthy controls (Love et al, 2009). However, this study used a different paradigm to induce endogenous opioid release (painful stimulus) and a different measure of impulsivity (NEO PI-R impulsivity) which may explain the differences in their findings compared with ours.

4.4.12. Current smoking and nicotine dependence as potential confounders of oral

11 dexamphetamine-induced [ C]carfentanil ∆BPND

Differences in the proportion of current smokers between our healthy controls and alcohol

11 dependent participants is a potential confounder of our [ C]carfentanil ∆BPND results.

There are a number of published studies examining the effects of current smoking and severity of nicotine dependence on endogenous opioid release (Domino et al, 2015; Kuwabara et al, 2014; Nuechterlein et al, 2016; Scott et al, 2007a). However, these studies used smoking nicotinised/denicotinised cigarettes to induce endogenous release rather than oral dexamphetamine and therefore are not comparable with our oral dexamphetamine challenge.

There were no significant differences in dexamphetamine-induced endogenous opioid release between current smokers and non-smokers in either healthy controls or alcohol dependent participants. Furthermore, the inclusion of current smoking status as a covariate

11 into our mixed model ANOVA examining differences in [ C]carfentanil ∆BPND between groups does not affect the significant results. Therefore, it is unlikely that the higher proportion of

209 current smokers in our alcohol dependent group is responsible for the significantly blunted endogenous opioid release in alcohol dependence in our data.

We also did not observe any significant associations between severity of nicotine dependence (FTND scores) in current smokers and dexamphetamine-induced endogenous opioid release. However, the numbers of current smoking participants, particularly in our healthy control group (n=3), are very small and these analyses are likely to be underpowered.

4.4.13. Effects of the OPRM1 A118G polymorphism on endogenous opioid release

There was no significant difference in endogenous opioid release between OPRM1 G-allele carriers (homozygous G:G or heterozygous G:A) compared with non G-allele carriers (homozygous A:A) in either healthy volunteers or alcohol dependent participants. Whilst the effect of the OPRM1 A118G polymorphism reducing MOR availability in G-allele carriers compared with non G-allele carriers is well characterised (Peciña et al, 2015b; Ray et al, 2011; Weerts et al, 2013), the effects on endogenous opioid release have been less well studied (Peciña et al, 2015a). The functional impact that the OPRM1 A118G polymorphism may have on the MOR receptor and opioidergic signalling, and whether it increases or reduces MOR receptor function, is still not fully understood (Bond et al, 1998; Kroslak et al, 2007; Mague et al, 2009; Peciña et al, 2015b; Zhang et al, 2005). However, our results show that whilst G- allele carriers have lower MOR availability, there does not appear to be a difference in the proportional displacement of [11C]carfentanil from these receptors following an oral dexamphetamine challenge, which may suggest that there is no impact of the OPRM1 A118G polymorphism on oral dexamphetamine-induced endogenous opioid release.

4.4.14. Limitations

A number of limitations to the analyses in this chapter are similar to those discussed in Chapter 3, Section 3.4.8., including partial volume effects and the non-linear registration

210 methods. Additional limitations related to the analyses and results in this chapter are discussed below.

Sample size The sample size calculation for this study was based on data from the oral dexamphetamine challenge and [11C]carfentanil PET in gambling disorder study (Mick et al, 2016). Apart from the [11C]carfentanil PET gambling disorder data, there were no other relevant published studies examining endogenous opioid release in alcohol dependence or other addictions. There is other PET literature examining dexamphetamine-induced endogenous dopamine release in addiction which has sample sizes comparable with ours (Boileau et al, 2014; Martinez et al, 2005; Narendran et al, 2014)

As with our results in Chapter 3, our sample was not designed to be powered to examine the associations between dexamphetamine-induced endogenous opioid release and clinical measures related to alcohol dependence such as duration of abstinence and severity of dependence, or other measures such as the OPRM1 genotype and current smoking status.

11 Negative [ C]carfentanil ∆BPND values or increases in MOR availability following dexamphetamine challenge

11 There are a number of participants in our dataset with negative [ C]carfentanil ∆BPND values. This is most evident in alcohol dependent participants, but there are also a small number of

11 healthy controls with negative ∆BPND values. Negative [ C]carfentanil ∆BPND values suggest that MOR availability following the oral dexamphetamine challenge is higher compared with MOR availability prior to the dexamphetamine challenge. This could indicate reductions in endogenous opioid concentrations during the post-dexamphetamine scan, however it is unclear what the basal concentration of endogenous opioids is in humans and what effect a reduction in basal tone would have on MOR availability. Furthermore, it is unclear why dexamphetamine would cause an increase in endogenous opioids in some individuals and a reduction in others.

211 Another possibility for these differences is the test-retest variability of the [11C]carfentanil

11 ligand. Hirvonen et al. (2009) found a mean [ C]carfentanil BPND (modelled with SRTM) test- retest variability of 5.4% averaged across 12 ROIs (range 2.7% to 8.2%) with a trend towards

11 higher [ C]carfentanil BPND in all brain regions during the retest scan (Hirvonen et al, 2009). The mechanism for this trend is unclear, although it is unlikely to be due to changes in the occipital reference tissue [11C]carfentanil specific binding as this was very consistent between test-retest scans (0.18% variability in distribution volume VT between scans modelled with 2- tissue compartment model) (Hirvonen et al, 2009). The test-retest scans in the Hirvonen et al. paper were carried out on the same day, similarly to our pre- and post-dexamphetamine

11 scans. It is possible that our participants with negative [ C]carfentanil ∆BPND have no endogenous opioid release following oral dexamphetamine challenge and the negative ∆BPND

11 represents the trend of higher [ C]carfentanil BPND in the second scan of the day as shown in the Hirvonen et al. test-retest study.

Simplified Reference Tissue Model (SRTM)

11 The limitation of using SRTM to model [ C]carfentanil BPND in our analyses is that any changes in specific or non-specific binding of the [11C]carfentanil in the reference tissue due to the oral dexamphetamine challenge cannot be accounted for in the model and will affect the BPND values in the other ROIs. Colasanti et al. (2012) found slightly lower [11C]carfentanil standardised uptake values (SUV) in the occipital cortex following 0.5 mg/kg oral dexamphetamine challenge. This reduction in occipital SUV may reduce the magnitude of

11 SRTM [ C]carfentanil ∆BPND values for other ROIs leading to more conservative estimates of endogenous opioid release (Colasanti et al, 2012).

Larger proportional reductions in [11C]carfentanil binding (for example due to higher endogenous opioid release) in the occipital reference tissue relative to other ROIs in alcohol

11 dependent participants compared with controls could result in blunted [ C]carfentanil ∆BPND values in alcohol dependent participants. It is unclear what mechanisms could lead to differences in the effect on dexamphetamine on occipital reference tissue [11C]carfentanil kinetics in alcohol dependent participants relative to other ROIs.

212 To fully investigate any potential effect of the dexamphetamine challenge on [11C]carfentanil kinetics in the occipital reference region would require arterial cannulation and arterial blood sampling to be carried out during the PET scan. This would allow changes in [11C]carfentanil kinetics in our target ROIs to be modelled without any potential confounding due to the use of a reference tissue. However, this would impact on the tolerability of the PET imaging protocol for our participants, and introduce additional risks associated with arterial cannulation.

Comparisons between alcohol dependent and gambling disorder participants There were some differences in the demographic characteristics of our alcohol dependent and gambling disorder participants which need to be considered when comparing dexamphetamine induced endogenous opioid release between these two groups. Firstly, the gambling disorder participants had substantially shorter durations of abstinence from gambling (mean 47 days) than alcohol dependent participants had from alcohol (mean 605 days). Alcohol dependent participants were required to be abstinent from alcohol for a minimum of 4 weeks. This was due to concerns about the potential effects of recent alcohol consumption and withdrawal on endogenous opioid signalling and therefore 4 weeks abstinence was the minimum required before an alcohol dependent participant was allowed to enrol in the study.

There were no similar concerns in the gambling disorder participants, and therefore participants with abstinence durations from gambling as low as 3 days were recruited to the Mick et al. (2012) study. Whilst gambling does not involve a pharmacological agent which induces a release of endogenous opioids, as is the case with alcohol use (Mitchell et al, 2012), it is not known what effect dependent gambling behaviour has on endogenous opioid signalling. It is also not known what impact abstinence has on dysregulated endogenous opioid signalling in alcohol dependence or gambling disorder. Therefore, it is possible that differences in the duration of abstinence between our alcohol dependent and gambling disorder participants may have a confounding effect on our comparisons of dexamphetamine induced endogenous opioid release between these two participant groups.

213 Secondly, whilst there were no participants with a current diagnosis of depression or anxiety, gambling disorder participants had higher mean depressive symptoms (BDI scores) and anxiety trait scores (STAI) than alcohol dependent participants, albeit these differences were non-significant. As discussed in Section 4.4.10. there is evidence that depression is associated with an anhedonic state and altered reward responses. Therefore, it is possible that higher depressive symptoms in gambling disorder participants may also be confounding comparisons of dexamphetamine induced endogenous with alcohol dependent participants.

4.5. Conclusion

At the start of this chapter it was hypothesised that abstinent alcohol dependent participants would have blunted oral dexamphetamine-induced endogenous opioid release compared with healthy controls, as previously shown in gambling disorder (Mick et al, 2016). As hypothesised we found blunted endogenous oral dexamphetamine-induced opioid release in alcohol dependence that was similar to that in gambling disorder suggesting that low endogenous opioid tone may be a common feature of both substance and behavioural addictions.

The mechanism of blunted oral dexamphetamine-induced endogenous opioid release in alcohol dependence is unclear but does not appear to be related to any of the alcohol dependence related clinical variables examined, including duration of abstinence, total high- risk alcohol consumption and severity. This may suggest that low endogenous opioid tone is due to ‘addiction’ rather than a consequence of the pharmacological effects of chronic substance use. It is also possible that low endogenous opioid tone is a pre-existing risk factor for developing alcohol dependence and by persisting into abstinence may be a vulnerability factor for relapse to alcohol use.

214 CHAPTER 5: COMBINING [11C]CARFENTANIL PET AND MONETARY INCENTIVE DELAY FMRI TO EXAMINE THE ASSOCIATIONS BETWEEN OPIOIDERGIC SIGNALLING AND REWARD RESPONSES IN ADDICTION

5.1. Introduction

5.1.1. Aims

This chapter examines the interrelation of MOR availability, dexamphetamine-induced endogenous opioid release and fMRI brain BOLD responses to financial reward anticipation during a monetary incentive delay (MID) fMRI task in healthy controls and individuals with alcohol dependence or gambling disorder. The aims of this chapter are: 1. To explore the link between dysregulated reward responses and endogenous opioidergic signalling in alcohol dependence by examining the associations between MID win>neutral anticipation BOLD responses and both MOR availability and oral dexamphetamine-induced endogenous opioid release in healthy controls and alcohol dependent individuals and comparing these associations between groups. 2. To explore if any associations between dysregulated reward responses and endogenous opioidergic signalling are due to chronic heavy alcohol use in alcohol dependence or due to ‘addiction’. This will be achieved by comparing associations between MID win>neutral anticipation BOLD responses and MOR availability and/or oral dexamphetamine-induced endogenous opioid release in gambling disorder participants with alcohol dependent participants.

5.1.2. Introduction to combined [11C]carfentanil PET and MID task fMRI

We have shown in Chapter 4 that endogenous opioid signalling is dysregulated in alcohol dependence. MORs play an important role in modulating mesocorticolimbic dopaminergic activity (Spanagel et al, 1992), and dysregulated endogenous opioid tone may affect this

215 activity. Reward anticipation during the MID task, as discussed in Chapter 1, Section 1.8, is associated with higher BOLD signal in key regions in the mesocorticolimbic network, for example the dorsal and ventral striatum, during financial reward anticipation (Knutson et al, 2000). Therefore, it might be expected that dysregulated endogenous signalling in alcohol dependence is associated with dysregulated MID reward anticipation BOLD responses.

One mechanism for MOR modulating mesocorticolimbic dopaminergic activity is via the ‘GABA-ergic brake’ in the ventral tegmental area (VTA). MOR agonism inhibits activity of the GABA-ergic interneurons. This then reduces GABA-ergic inhibition of dopaminergic neurons resulting in higher mesocorticolimbic dopaminergic activity (Spanagel et al, 1992). It might be expected that higher MOR availability, reflecting a higher sensitivity of MOR signalling, will be associated with higher MID reward anticipation responses. For the combined PET fMRI analyses the following ROIs have been selected a priori: NAcc, caudate, putamen and ventral pallidum. These four regions were selected for the following reasons: - The NAcc and ventral pallidum are brain regions where opioid signalling plays a key role in modulating the hedonic value of reward (Berridge and Kringelbach, 2015). - Areas of the caudate and putamen show significant MID win>neutral BOLD contrast in the ALE metanalysis conducted by McGonigle et al. (2017).

Endogenous opioid signalling also plays an important role in modulating the hedonic responses and valence (i.e. positive or negative anticipation) attribution to rewards (Berridge and Kringelbach, 2015). There is evidence of blunted MID win>neutral anticipation BOLD responses in alcohol dependence (Balodis and Potenza, 2015; Nestor et al, 2017) and this may be associated with the blunted endogenous tone in alcohol dependence shown in Chapter 4. For example, individuals with lower endogenous opioid tone may be expected to have more blunted MID win>neutral anticipation BOLD contrast.

Alcohol consumption is associated with endogenous opioid release (Mitchell et al, 2012), and the chronic alcohol consumption in alcohol dependence may lead to dysregulated endogenous opioid signalling (Hermann et al, 2017). We did not observe the higher MOR receptor availability previously described in the published literature during early abstinence (Heinz et al, 2005; Weerts et al, 2011; Williams et al, 2009) in our long-term abstinent alcohol

216 dependent participants (see Chapter 3). However, it is still possible that chronic alcohol consumption has had some effect on their endogenous opioid signalling, for example low endogenous opioid tone (see Chapter 4). Therefore, it would be important to compare alcohol dependent participants not only with healthy controls but also a non-substance associated addiction, in this case gambling disorder, to better understand if any dysregulation in the associations between endogenous signalling and MID reward anticipation responses are due to addiction or due to an effect of chronic heavy alcohol use. Furthermore, in gambling disorder financial rewards should be more salient than in alcohol dependence, as winning and losing money is a key component of this behavioural addiction (American Psychiatric Association, 2013). Therefore, by comparing gambling disorder and alcohol dependent participants it may be possible to explore the impact of salience on the associations between MID win>neutral anticipation BOLD responses, MOR availability and endogenous opioid tone in addiction.

The OPRM1 A118G polymorphism G-allele has been associated with lower MOR availability (Peciña et al, 2015b; Ray et al, 2011) and higher striatal BOLD responses to alcohol cues in abstinent alcohol dependent participants (Bach et al, 2015). Given the evidence that this polymorphism affects both the endogenous MOR system and striatal BOLD and dopamine responses, it will be investigated whether the OPRM1 polymorphism has a confounding effect on any associations between MID win>neutral BOLD responses and MOR availability or dexamphetamine-induced endogenous opioid release.

5.1.3. Hypotheses

1. MID win>neutral anticipation BOLD contrast will be lower in alcohol dependent participants compared with healthy controls. Gambling disorder participants will have higher MID win>neutral anticipation BOLD contrast due to the higher salience of financial rewards in gambling disorder. 2. High MOR availability will be associated with higher MID win>neutral anticipation BOLD contrast in all participants.

217 3. A greater degree of blunted oral dexamphetamine challenge endogenous opioid release will be associated with more blunted win>neutral anticipation BOLD contrast in alcohol dependent participants.

5.2. Methods

5.2.1. Study sample

MID fMRI data were collected for 28 healthy controls, 13 alcohol dependent and 20 gambling disorder participants. As stated in Chapter 2, Section 2.13.2. one healthy control was excluded due to low task accuracy. Of these remaining participants 13 healthy controls, 13 alcohol dependent and 15 gambling disorder participants also had [11C]carfentanil PET data available.

Combined OPRM1 genotype and MID fMRI data were available for 26 healthy controls, 13 alcohol dependent and 19 gambling disorder participants. Combined OPRM1 genotype, [11C]carfentanil PET and MID fMRI data were available for 13 healthy controls, 13 alcohol dependent and 15 gambling disorder participants.

Further details of study sample are available in Section 2.2..

5.2.2. Graphical representation of whole-brain analysis results

Results from whole-brain analyses were shown graphically by overlaying significant clusters (e.g. z>2.3 or z>3.1) on an MNI152 template brain. A number of slices are shown and each slice has an associated MNI coordinate for the sagittal, axial or coronal plane (X, Y and Z respectively). All whole-brain figures are presented in ‘neurological view’ – i.e. in coronal and axial planes the right side of the figure represents the right side of the brain, and left side of the figure represents the left side of the brain.

218 The clusters in the results images have a colour gradient representing the voxel-wise z- statistic values and a colour bar is included with each image for reference. Each set of images represents a single FSL FEAT analysis statistic (e.g. t-test or correlation coefficient) and the z- statistic values are from this statistical test in FSL FEAT. Due to the nature of the FSL output each statistic has a separate whole-brain cluster output and the z-values in this output are positive. These z-statistic values are positive, and the interpretation of the result depends on the contrast of the EVs used to model the result. For example, in Figure 5.1. a significant result from Contrast 3 (C3) represents a negative correlation as the EV for this contrast is weighted -1. To address this, different colour palettes were used in whole-brain statistic images with hot/orange representing positive correlations and cold/blue representing negative correlations. Other statistics, including t-tests and interactions have been represented with a green palette.

Finally, all results are shown as z>2.3. When a result is also significant at z>3.1 these clusters are shown in a violet colour.

Figure 5.1. – Example FSL FLAME model examining correlations

11 between [ C]carfentanil BPND and MID win>neutral anticipation BOLD contrast: Positive and negative correlations between ventral pallidum

11 [ C]carfentanil BPND and MID win>neutral anticipation BOLD contrast in 15 gambling disorder participants.

219 5.3. Results

5.3.1. MID fMRI dataset demographics

Demographic details for the MID fMRI dataset (27 healthy controls, 13 alcohol dependent participants, 20 gambling disorder participants) are shown in Table 5.1..

220 Table 5.1. – Demographic measures for fMRI dataset: (mean ±SD) compared between healthy controls (HC), alcohol dependent (AD) and gambling disorder (GD) participants including results from ANOVAs examining differences between groups and post-hoc independent sample t-tests or Mann Whitney U test for BDI and alcohol abstinence (Bonferroni corrected significance threshold p<0.017). Data shown for 27 healthy controls, 13 alcohol dependent and 20 gambling disorder participants unless otherwise indicated. HC AD GD ANOVA Variable (total 27) (total 13) (total 20) p value 37.4 (±11.7) 46.6 (±7.3) 34.5 (±7.4) Age 0.003 * *, † †

BMI 24.3 (±3.4) 26.7 (±3.7) 26.2 (±4.5) 0.109

15 (±26) 605 (±867) 11 (±14) Alcohol abstinence (days) <0.001 * *, † † 7.8 (±7.5) 0 (±0) 11.1 (±7.9) Current alcohol use (units per week) <0.001 * *, † † Gambling abstinence (days) N/A N/A 46.5 (±43.3) N/A (GD only) 7 7 7 Current Smokers N/A (26%) (54%) (35%) Cigarettes per day in smokers 8.4 (±4.2) 10.6 (±7.7) 12.7 (±6.9) 0.476 (7 HC, 7 AD, 7 GD) FTND in smokers 2.0 (±2.0) 3.7 (±3.1) 4.6 (±1.7) 0.142 (7 HC, 7 AD, 7 GD) Pack years in current and ex-smokers 7.2 (±7.4) 23.6 (±14.5) 12.2(±6.1) 0.002 (12 HC, 11 AD, 9 GD) * * STAI 31.5 (±9.3) 43.1 (±11.5) 37.2 (±5.9) 0.001 (26 HC) ** ** 1.4 (±2.6) 7.5 (±7.3) BDI 3.3 (±3.6) <0.001 ** ** 52.7 (±9.5) 49.2 (±4.8) 69.7 (±9.7) BIS total score <0.001 ** † **, † 21.3 (±6.0) 27.4 (±4.1) 31.0 (±6.0) Negative Urgency <0.001 *, ** * ** Lack of 20.5 (±5.0) 24.3 (±5.3) 22.8 (±3.5) 0.036 premeditation ** ** UPPS-P Lack of (26 HC, 12 AD) 18.5 (±3.9) 18.5 (±3.8) 20.7 (±5.0) 0.203 perseverance Sensation Seeking 32.9 (±7.9) 33.1 (±6.0) 34.1 (±6.9) 0.856 21.7 (±6.7) 30.8 (±4.9) 27.2 (±8.5) Positive Urgency 0.001 *, ** * ** OPRM1 G-allele present (G:G or G:A) 8 4 4 N/A (26 HC, 19 GD) (31%) (31%) (21%) Post-hoc tests: * HC vs. AD Bonferroni corrected significance threshold p<0.017 ** HC vs. GD Bonferroni corrected significance threshold p<0.017 † AD vs. GD Bonferroni corrected significance threshold p<0.017

221 5.3.2. fMRI MID dataset task behavioural measures

Behavioural measures for the MID task are shown in Table 5.2.. Typically, reaction times and accuracy were lowest in healthy controls and highest in gambling disorder participants. There were significant differences between healthy controls compared with gambling disorder participants in all measures except for neutral task reaction time.

Table 5.2. – MID task behavioural measures in fMRI dataset: (mean ±SD) compared between 27 healthy controls, 13 alcohol dependent and 20 gambling disorder participants including results from ANOVA examining differences between groups and post-hoc independent sample t-tests (Bonferroni corrected significance threshold p<0.017). HC AD GD ANOVA Behavioural Variable (total 27) (total 13) (total 20) p value 63.7 (±7.3) 69.7 (±5.1) Win trial 67.5 (±6.9) 0.010 ** ** 54.3 (±16.1) 64.0 (±8.2) Accuracy Neutral trial 63.0 (±7.6) 0.019 ** ** 56.5 (±14.7) 68.3 (±13.4) Loss trial 59.0 (±18.8) 0.033 ** ** 241.0 (±22.1) 221.5 (±18.3) Win trial 225.6 (±21.2) 0.006 ** ** 0.027 Reaction time Neutral trial 260.9 (±34.4) 238.8 (±25.4) 240.0 (±25.2)

238.6 (±24.6) 220.3 (±21.5) Loss trial 227.2 (±24.7) 0.034 ** ** 8.9 (±2.0) 10.7 (±1.6) Total amount won (£) 9.7 (±2.3) 0.012 ** ** Post-hoc tests: ** HC vs. GD Bonferroni corrected significance threshold p<0.017

Seven mixed model ANOVAs were carried out in healthy controls, one for each of the seven behavioural measures, to examine if there were any associations between behavioural measures and MID win>neutral anticipation %BOLD signal change across the 4 selected fROIs. There were no significant between-subject effects of any of the behavioural measures on MID win>neutral anticipation %BOLD signal change indicating no effect of task performance on MID win>neutral anticipation BOLD contrast.

222 5.3.3. Group comparison of MID win>neutral anticipation BOLD contrast: healthy controls, alcohol dependent and gambling disorder participants

MID win>neutral anticipation %BOLD signal change values extracted from the selected NAcc, caudate, putamen and ventral pallidum fROIs (see Section 2.14.1. for details) were compared between the three participant groups (27 healthy controls, 13 alcohol dependent and 20 gambling disorder participants) using a mixed model ANOVA. This showed no significant between-subject effect of Status or Status x BOLD fROI interaction (Table 5.3.).

Table 5.3. – Mixed model ANOVA examining the within-subject effect of BOLD fROI (4 regions) and between-subject effect of Status (healthy control, alcohol dependent or gambling disorder) on MID win>neutral anticipation %BOLD change in 27 healthy controls, 13 alcohol dependent and 20 gambling disorder participants. Effects F-ratio (effect df, error df) p value BOLD fROI 1.0 (2.1, 121.7) 0.389 Within-subject factors BOLD fROI x 1.6 4.3, 121.7 0.178 Status Between-subject factors Status 1.3 (2, 57) 0.285

There was a trend towards highest %BOLD signal change values in gambling disorder participants and lowest %BOLD signal change values in alcohol dependent participants compared with healthy controls (Figure 5.2.).

Healthy Controls Figure 5.2. - Mean (±SD) MID 0.8 Alcohol win>neutral anticipation %BOLD Dependent 0.6 change across the 3 participant Gambling Disorder groups (27 HC, 13 AD and 20 GD) in 0.4 4 fROIs: NAcc, caudate, putamen %BOLD Change 0.2 and ventral pallidum.

0.0 NAcc Caudate Putamen V. Pal

223 An FSL FLAME model was used to further explore if there were any significant differences in MID win>neutral anticipation BOLD contrast between groups. The FSL FLAME ANOVA model showed no between-subject effect of Status on MID win>neutral anticipation BOLD contrast.

5.3.4. Combined MID fMRI and [11C]carfentanil PET dataset demographics

Of the participants who completed the MID task, [11C]carfentanil PET data were also available for 13 healthy controls, 13 alcohol dependent participants and 15 gambling disorder participants. Demographic details for these participants are shown in Table 5.4..

224 Table 5.4. – Demographic measures (mean ±SD) in combined PET and fMRI dataset: compared between healthy controls (HC), alcohol dependent (AD) and gambling disorder (GD) participants including results from ANOVAs examining significant differences between groups and post-hoc independent sample t-tests or Mann Whitney U test for BDI and Alcohol Abstinence (Bonferroni corrected significance threshold p<0.017). Data shown for 13 healthy controls, 13 alcohol dependent and 15 gambling disorder participants unless otherwise indicated. HC AD GD ANOVA Variable (total 13) (total 13) (total 15) p value 46.6 (±7.3) 34.3 (±7.3) Age 39.7 (±11.8) 0.003 † † BMI 25.1 (±4.6) 26.7 (±3.7) 26.8 (±5.0) 0.535 11.5 (±21.6) 605 (±867) 8.9 (±7.9) Alcohol abstinence (days) 0.003 * *, † † Current alcohol Use 7.8 (±8.4) 0 (±0) 11.3 (±7.5) <0.001 (units per week) * *, † † Gambling abstinence (days) N/A N/A 43.1 (±42.6) N/A (GD only) 3 7 4 Current smokers N/A (23%) (54%) (27%) Cigarettes per day in smokers 0.797 10.0 (±5.0) 10.6 (±7.7) 13.5 (±9.4) (3 HC, 7 AD, 4 GD) FTND in smokers 3.0 (±2.6) 3.7 (±3.1) 5.3 (±1.7) 0.530 (3 HC, 7 AD, 4 GD) Pack years in current and ex-smokers 10.2 (±8.1) 23.6 (±14.5) 16.0 (±6.4) 0.236 (3 HC, 11 AD, 4 GD) 30.2 (±7.3) 44.5 (±12.7) STAI 37.2 (±5.9) 0.001 ** ** 0.8 (±2.0) 8.1 (±8.0) BDI 3.3 (±3.6) 0.003 ** ** 50.0 (±8.1) 49.2 (±4.8) 70.8 (±10.6) BIS total score <0.001 ** † **, † 21.5 (±5.4) 27.4 (±4.1) 32.3 (±5.7) Negative Urgency <0.001 *, ** * ** Lack of 21.8 (±5.1) 22.8 (±3.5) 24.3 (±5.7) 0.412 premeditation UPPS-P Lack of 18.8 (±4.0) 18.5 (±3.8) 20.5 (±4.5) 0.407 (12 AD) perseverance Sensation Seeking 33.0 (±7.8) 33.1 (±6.0) 35.2 (±6.8) 0.637 21.8 (±6.4) 30.8 (±4.9) 28.3 (±8.3) Positive Urgency 0.006 *, ** * ** 4 4 4 OPRM1 G-allele present (G:G or G:A) N/A (31%) (31%) (27%) Post-hoc tests: * HC vs. AD Bonferroni corrected significance threshold p<0.017 ** HC vs. GD Bonferroni corrected significance threshold p<0.017 † AD vs. GD Bonferroni corrected significance threshold p<0.017

225 5.3.5. Combined [11C]carfentanil PET and MID fMRI dataset task behavioural measures

MID behavioural measures for the subjects included in the combined MID fMRI and [11C]carfentanil PET analysis are shown in Table 5.5.. Typically, reaction times and accuracy were lowest in healthy controls and highest in gambling disorder participants, however, there were no significant differences in behavioural measures between groups (independent sample t-tests uncorrected p>0.05).

Table 5.5. – MID task behavioural measures in combined PET and fMRI dataset: (mean ±SD) compared between 13 healthy controls, 13 alcohol dependent and 15 gambling disorder participants including results from ANOVA examining significant differences between groups and post-hoc independent sample t-tests (Bonferroni corrected significance threshold p<0.017). AD HC GD ANOVA Behavioural Variable (total 13) (total 13) (total 15) p value Win trial 67.5 (±6.9) 66.0 (±4.7) 69.8 (±5.7) 0.237 Accuracy Neutral trial 63.0 (±7.6) 54.7 (±18.1) 63.0 (±8.6) 0.142 Loss trial 59.0 (±18.8) 60.9 (±14.2) 67.8 (±14.7) 0.314 Win trial 225.6 (±21.2) 235.5 (±19.1) 221.8 (±20.0) 0.202 Reaction Time Neutral trial 238.8 (±25.4) 253.8 (±29.5) 243.5 (±25.0) 0.352 Loss trial 227.2 (±24.7) 232.5 (±22.8) 222.1 (±23.8) 0.520 Total amount won (£) 9.7 (±2.3) 9.5 (±1.6) 10.6 (±1.8) 0.266 No significant post-hoc tests (all uncorrected p>0.05).

5.3.6. Combined MID win>neutral anticipation BOLD contrast and baseline

11 [ C]carfentanil BPND – ROI analysis

Pearson’s correlational analyses were carried out to examine both intra- and inter-regional

11 correlations between baseline [ C]carfentanil BPND and MID win>neutral anticipation %BOLD signal change across the four selected ROIs/fROIs (Table 5.6.).

226 11 Table 5.6. – Correlations between [ C]carfentanil BPND and MID win>neutral anticipation %BOLD signal change: Pearson’s R values in 4 selected ROIs/fROIs in 13 healthy controls, 13 alcohol dependent and 15 gambling disorder participants. MID %BOLD Healthy Alcohol Gambling signal change control dependent disorder fROI

NAcc BPND Pearson’s R values NAcc -0.169 -0.050 0.104 Caudate -0.124 0.072 0.069 Putamen -0.249 -0.134 0.127 V. Pall -0.116 -0.220 0.174

Caudate BPND Pearson’s R values NAcc 0.201 -0.304 -0.018 Caudate 0.144 -0.002 -0.024 Putamen 0.09 -0.239 -0.048 V. Pall 0.133 -0.459 0.119

Putamen BPND Pearson’s R values NAcc 0.254 -0.613* -0.239 Caudate 0.148 -0.657* -0.362 Putamen 0.212 -0.688** -0.353 V. Pall 0.218 -0.536 -0.121

Ventral Pallidum BPND Pearson’s R values NAcc 0.037 -0.483 -0.485 Caudate 0.107 -0.223 -0.559* Putamen 0.003 -0.376 -0.549* V. Pall -0.141 -0.407 -0.390 Significance: *uncorrected p<0.05, **uncorrected p<0.01

These analyses showed correlations in alcohol dependent participants between putamen

BPND and %BOLD signal change in both the NAcc and putamen ROIs (Figure 5.3.). There were also correlations in gambling disorder participants between ventral pallidum BPND and %BOLD signal change in caudate and putamen ROIs (Figure 5.4.). However, these correlations in both alcohol dependent and gambling disorder participants did not survive Bonferroni or P-plot Hochberg correction for multiple comparisons. There were no significant correlations in healthy controls.

227 0.8 Figure 5.3. – Negative correlation between 11 putamen [ C]carfentanil BPND and putamen 0.6 win>neutral anticipation %BOLD signal change 0.4 in 13 alcohol dependent participants (Pearson’s 0.2 R= -0.688, p=0.009). Signal Change Putamen %BOLD 0.0 -0.2 0 1.4 1.6 1.8 2.0 2.2 ¹¹ Putamen [ C]carfentanil BPND

1.2 Figure 5.4. – Negative correlation between 11 1.0 ventral pallidum [ C]carfentanil BPND and 0.8 Putamen win>neutral anticipation %BOLD 0.6 signal change in 15 gambling disorder 0.4 participants (Pearson’s R= -0.549, p=0.034). Signal Change 0.2 Putamen %BOLD 0.0 -0.2 0 2.0 2.2 2.4 2.6 2.8 3.0 ¹¹ V. Pal [ C]carfentanil BPND

5.3.7. Combined MID win>neutral anticipation BOLD contrast and baseline

11 [ C]carfentanil BPND – FSL FLAME analysis

Whole-brain FSL FLAME analyses were carried out to explore the correlations observed between MID win>neutral anticipation BOLD contrast and putamen BPND in alcohol dependent participants and ventral pallidum BPND in gambling disorder participants (see Table 5.6.).

The only significant result at z>3.1 was a negative correlation between MID win>neutral anticipation BOLD contrast and putamen BPND in alcohol dependent participants. At z>2.3 there were more clusters with significant negative correlation between MID win>neutral anticipation BOLD contrast and putamen BPND in alcohol dependent participants (Figure 5.5.). In gambling disorder there were no significant correlations at z>3.1, although there was a

228 11 trend of negative correlations between ventral pallidum [ C]carfentanil BPND and MID win>neutral anticipation BOLD contrast at z>2.3 (Figure 5.6.).

AD Putamen BPND negative correlation

z 48 z 23 Figure 5.5. – Negative correlations

11 between putamen [ C]carfentanil BPND and MID win>neutral anticipation BOLD

Z-30 contrast in 13 alcohol dependent Z3 participants: Results from FSL FLAME Z23 model Violet colour indicates clusters Z48 significant at cluster corrected z>3.1 (3 clusters: 402, 286 and 207 clusters – bilateral post- and pre-central gyri). z 3 z -30

Blue colour represents results at cluster corrected z>2.3. (4 clusters: 11175, 2338, 871 and 540 clusters – including bilateral dorsal striatum, occipital cortex and frontal cortex, right insular and left cerebellum).

Z Value 0 1 2 3 4

229 GD VPal BPND negative correlation

z 48 z 28 Figure 5.6. – Negative correlations between ventral pallidum

11 [ C]carfentanil BPND and MID Z-14 win>neutral anticipation BOLD contrast Z8 in 15 gambling disorder participants: Z28 Results from FSL FLAME model (cluster Z48 corrected z>2.3). (two clusters: 8133 and 835 voxels – including bilateral dorsal striatum, thalamus, anterior z 8 z -14 cingulate, frontal cortex and cerebellum and right frontal cortex).

Z Value 0 1 2 3 4

5.3.8. Examining group differences in correlations between MID win>neutral

11 anticipation BOLD contrast and baseline [ C]carfentanil BPND

FSL FLAME interaction analyses were carried out to examine if the negative correlations

11 between MID win>neutral anticipation BOLD contrast and putamen [ C]carfentanil BPND observed in alcohol dependent participants in Section 5.3.7. were significantly different from the lack of correlations in healthy controls and gambling disorder participants.

The FLAME analysis examining differences in the correlations between MID win>neutral

11 anticipation BOLD contrast and putamen [ C]carfentanil BPND in alcohol dependent participants compared with healthy controls did not show any significant results at z>3.1.

230 However, the FLAME analysis examining differences in the correlations between MID

11 win>neutral anticipation BOLD contrast and putamen [ C]carfentanil BPND in alcohol dependent participants compared with gambling disorder participants showed significant clusters at z>3.1 (Figure 5.7.).

AD & GD Putamen BPND Interaction

z 46 z 18 Figure 5.7. - Differences in correlation

11 between putamen [ C]carfentanil BPND and MID win>neutral anticipation Z-4 BOLD contrast in 13 alcohol dependent Z-4 participants compared with 15 Z18 gambling disorder participants. Z46 Violet colour indicates significant results (cluster corrected z>3.1) in two bilateral post-central/supramarginal z 4 z -4 gyrus clusters (2 clusters: 231 and 199 voxels).

Green colour indicates clusters at cluster corrected z>2.3).

Z Value 0 1 2 3 4

Figure 5.8. shows the win>neutral anticipation %BOLD signal change values extracted from

11 the bilateral z>3.1 clusters (see Figure 5.7.) plotted against putamen [ C]carfentanil BPND values in alcohol dependent and gambling disorder participants to illustrate the significant differences in the correlation between the two groups.

231 Figure 5.8. – Correlations between 1.2 Alcohol 11 putamen [ C]carfentanil BPND and Dependent 0.8 Gambling MID win>neutral anticipation %BOLD Disorder 0.4 change (extracted from z>3.1 bilateral clusters in Figure 5.7.) in 13 alcohol 0.0

%BOLD Signal Change dependent and 15 gambling disorder

-0.4 participants with fitted linear 0 1.4 1.6 1.8 2.0 2.2 regression lines (Pearson’s R=-0.89, ¹¹ Putamen [ C]carfentanil BPND p<0.001 and R=0.40, p=0.152 respectively).

5.3.9. Summary of results from FSL FLAME analyses examining correlations between

11 MID win>neutral anticipation BOLD contrast and baseline [ C]carfentanil BPND

Table 5.7. – Summary results of FSL FLAME models examining the correlations between MID win>neutral

11 anticipation BOLD contrast and baseline [ C]carfentanil BPND (cluster corrected z>3.1): in healthy controls, alcohol dependence and gambling disorder (Section 5.3.7. and Section 5.3.8). Correlation with Comparisons Participant MID win>neutral [11C]Carfentanil with other group anticipation BOLD BPND ROI groups contrast

Healthy controls None significant N/A

Negative Alcohol dependent Putamen † Correlation

Gambling disorder None significant N/A †

Significant FLAME analysis group differences in the correlations between MID win>neutral anticipation

11 BOLD contrast and [ C]carfentanil BPND: †AD vs. GD

232 5.3.10. Combined MID win>neutral anticipation BOLD contrast and

11 dexamphetamine-induced [ C]carfentanil ∆BPND – ROI analysis

11 Similarly to the ROI-wise baseline [ C]carfentanil BPND analyses in Section 5.3.6., Pearson’s correlations were carried out to examine both intra- and inter-regional correlations between

11 [ C]carfentanil ∆BPND and MID win>neutral anticipation %BOLD signal change across the four selected ROIs/fROIs (Table 5.8.).

11 Table 5.8. – Correlations between [ C]carfentanil ∆BPND and MID win>neutral %BOLD signal change: Pearson’s R values in 4 selected ROIs/fROIs in 13 healthy controls, 13 alcohol dependent and 15 gambling disorder participants. MID %BOLD Healthy Alcohol Gambling signal change controls dependent disorder fROI

NAcc ∆BPND Pearson’s R values NAcc -0.018 0.213 -0.078 Caudate -0.107 0.233 -0.039 Putamen -0.184 0.350 -0.029 V. Pall 0.044 0.384 -0.146

Caudate ∆BPND Pearson’s R values NAcc -0.022 -0.098 -0.004 Caudate -0.171 0.054 0.009 Putamen 0.018 -0.091 -0.085 V. Pall -0.048 -0.232 -0.155

Putamen ∆BPND Pearson’s R values NAcc -0.031 0.059 -0.200 Caudate -0.167 0.148 -0.284 Putamen -0.003 0.009 -0.266 V. Pall -0.003 -0.031 -0.314

Ventral Pallidum ∆BPND Pearson’s R values NAcc -0.524 0.492 -0.467 Caudate -0.724** 0.356 -0.421 Putamen -0.624* 0.472 -0.490 V. Pall -0.516 0.496 -0.479 *uncorrected p<0.05, **uncorrected p<0.01

There were correlations (uncorrected p<0.05) between ventral pallidum ∆BPND and caudate and putamen MID win>neutral anticipation %BOLD signal change in healthy controls (Figure 5.9.). However, these correlations in healthy controls did not survive Bonferroni or P-plot

233 Hochberg correction for multiple comparisons. There were no significant ROI-wise correlations in alcohol dependent or gambling disorder participants.

Figure 5.9. – Correlation between ventral 0.3 11 pallidum [ C]carfentanil ∆BPND and caudate 0.2 win>neutral anticipation %BOLD signal change in

0.1 13 healthy controls (Pearson’s R=-0.724, p=0.005). Signal Change Caudate %BOLD 0.0

-0.1 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 ¹¹ V. Pal [ C]carfentanil ∆BPND

5.3.11. Combined MID win>neutral anticipation BOLD contrast and

11 dexamphetamine-induced [ C]carfentanil ∆BPND – FSL FLAME analyses

An FSL FLAME analysis was carried out to explore the correlations between ventral pallidum

11 [ C]carfentanil ∆BPND and MID win>neutral anticipation %BOLD signal change observed in healthy controls in Section 5.3.10. (see Table 5.8.). This did not show any significant correlations at z>3.1, although some negative correlations between MID win>neutral anticipation BOLD contrast and ventral pallidum ∆BPND in healthy controls were observed at z>2.3 (Figure 5.10.).

234 X26 Y19 Z-1 HV VPal delta BPND negative correlation

z -1 y 19 x 26

Z Value 0 1 2 3 4 11 Figure 5.10. – Negative correlations between ventral pallidum [ C]carfentanil ∆BPND and MID win>neutral anticipation BOLD contrast in 13 healthy controls: results from FSL FLAME model (cluster corrected z>2.3) (1 cluster: 1296 voxels – including right putamen and insula).

Exploratory FLAME analyses were carried out to examine correlations between MID win>neutral anticipation BOLD contrast and putamen and ventral pallidum [11C]carfentanil

∆BPND in alcohol dependent and gambling disorder participants respectively. These exploratory analyses were decided based upon the significant correlations between MID

11 win>neutral anticipation BOLD contrast and baseline [ C]carfentanil BPND in these regions in alcohol dependent and gambling disorder participants shown in Section 5.3.6. and Section 5.3.7..

There were significant negative correlations between win>neutral anticipation and ventral pallidum ∆BPND in the gambling disorder participant FLAME model at z>3.1 (Figure 5.11.). There were no significant results in the FLAME model examining correlations between

11 win>neutral anticipation BOLD and putamen [ C]carfentanil ∆BPND in alcohol dependent participants.

235 GD VPal deltaBPND negative correlation

z 44 z 15 Figure 5.11. – Negative correlations between ventral pallidum

11 [ C]carfentanil ∆BPND and MID Z-42 win>neutral anticipation BOLD contrast Z-6 in 15 gambling disorder participants: Z15 Z44 Violet colour indicates significant results at cluster corrected z>3.1 (1 cluster: 247 voxels – frontal pole).

z -6 z -42 Blue colour is results from FSL FLAME model at cluster corrected z>2.3. (5 clusters: 4404, 2823, 2566, 1972 and 977 voxels – including bilateral cerebellum and frontal cortex).

Z Value 0 1 2 3 4

5.3.12. Examining group differences in correlations between MID win>neutral

11 anticipation BOLD contrast and [ C]carfentanil ∆BPND

FSL FLAME interaction analyses were carried out to examine if the negative correlation between MID win>neutral anticipation BOLD contrast and ventral pallidum [11C]carfentanil

∆BPND in gambling disorder was significantly different compared with alcohol dependent participants or healthy controls.

There were no significant results in the FLAME analysis comparing ventral pallidum

11 [ C]carfentanil ∆BPND and MID win>neutral anticipation BOLD contrast correlations between gambling disorder participants and healthy controls. There was, however, a significant result in the FSL FLAME analysis examining differences in the correlations between MID win>neutral

236 11 anticipation BOLD contrast and ventral pallidum [ C]carfentanil ∆BPND in gambling disorder X53 participantsY6 compared with alcohol dependent participants at z>3.1. (Figure 5.12.). Z3 AD - GD ventral pallidum ∆BPND

z 3 y 6 x 53

Z Value 0 1 2 3 4

11 Figure 5.12. – Differences in correlation between ventral pallidum [ C]carfentanil ∆BPND and MID win>neutral anticipation BOLD contrast in 13 alcohol dependent participants compared with 15 gambling disorder participants: Violet colour indicates results significant at cluster corrected z>3.1 (1 cluster: 309 voxels – right insula and inferior frontal gyrus). Green cluster is results at cluster corrected z>2.3 (2 clusters: 2003, 1717 voxels – including bilateral dorsal striatum, insula and inferior frontal gyrus).

MID win>neutral anticipation %BOLD signal change values were extracted for both gambling disorder and alcohol dependent participants from the z>3.1 right insula cluster in Figure 5.12.

11 and plotted against ventral pallidum [ C]carfentanil ∆BPND values in Figure 5.13. to illustrate the significant differences in correlation between these two groups.

Figure 5.13. – Correlations between 1.5 Alcohol ventral pallidum [11C]carfentanil Dependent 1.0 Gambling ∆BPND and MID win>neutral Disorder 0.5 anticipation %BOLD signal change (extracted from right insula z>3.1 0.0

% BOLD signal change cluster in Figure 5.12.) in 15 gambling

-0.5 disorder and 13 alcohol dependent -0.3 -0.2 -0.1 0.0 0.1 0.2 participants with fitted linear ¹¹ V. Pal [ C]carfentanil ∆BPND regression lines (R=0.69, p=0.008 and R=-0.58, p=0.022 respectively).

237 5.3.13. Summary of results from FSL FLAME analyses examining correlations between MID BOLD win>neutral anticipation and oral dexamphetamine-

11 induced [ C]carfentanil ∆BPND

Table 5.9. – Summary results of FSL FLAME models examining the correlations between MID win>neutral

11 anticipation BOLD contrast and [ C]carfentanil ∆BPND (cluster corrected z>3.1): in healthy controls, alcohol dependence and gambling disorder participants (Section 5.3.11. and Section 5.3.12). Correlation with MID Comparison win>neutral [11C]Carfentanil Group with other anticipation BOLD ∆BPND ROI groups contrast

Healthy controls None significant N/A

Alcohol dependent None significant N/A †

Gambling disorder Negative Correlation Ventral Pallidum †

Significant FLAME whole-brain group differences in the correlations between of MID win>neutral

11 anticipation BOLD contrast and [ C]carfentanil ∆BPND: †AD vs. GD

5.3.14. Examining the potential confounding effects of the OPRM1 polymorphism on MID win>neutral anticipation BOLD contrast – ROI analyses

OPRM1 Genotype data were available for 26 healthy controls, 13 alcohol dependent and 19 gambling disorder participants who completed the MID task (see Tables 5.1. and 5.4 for allele frequencies). The effect of OPRM1 Genotype G-allele on MID win>neutral anticipation %BOLD change in four selected fROIs (NAcc, caudate, putamen, ventral pallidum) was investigated using a mixed model ANOVA. This did not show any significant between-subject effect of OPRM1 genotype or OPRM1 genotype x Status or OPRM1 genotype x BOLD ROI interactions (Table 5.10.).

238 Table 5.10. – Mixed model ANOVA examining the within-subject effect of BOLD ROI (MID win>neutral anticipation %BOLD signal change in 4 fROIs) and the between-subject effects of Status (healthy control, alcohol dependent or gambling disorder) and OPRM1 G-allele (G:G or G:A genotype) in 26 healthy controls, 13 alcohol dependent and 19 gambling disorder participants. Effects F-ratio (effect df, error df) p value BOLD ROI 0.4 (2.1, 109.6) 0.657 BOLD ROI x 0.6 (2.1, 109.6) 0.575 OPRM1 G-allele BOLD ROI x Within-subject factors 0.8 (4.2, 109.6) 0.536 Status BOLD ROI x OPRM1 G-allele x 0.7 (4.2, 109.6) 0.612 Status OPRM1 G-allele 0.6 (1, 52) 0.432 Status 1.0 (2, 52) 0.372 Between-subject factors OPRM1 G-allele x 0.7 (2, 52) 0.524 Status

Figure 5.14. – Mean (±SD) NAcc MID 0.8 Healthy Controls win>neutral anticipation %BOLD change in 0.6 Alcohol A-allele homozygous (A:A) and G-allele Dependent carriers (G:G/G:A): in healthy controls, 0.4 Gambling Disorder alcohol dependent and gambling disorder NAcc %BOLD signal change 0.2 participants.

0.0 A:A G:G/G:A

5.3.15. Examining the potential confounding effects of the OPRM1 polymorphism on MID win>neutral anticipation BOLD contrast – FSL FLAME analyses

The effect of the OPRM1 G-allele on MID win>neutral anticipation BOLD contrast was further explored using FSL FLAME independent sample t-tests comparing MID win>neutral anticipation BOLD contrast between OPRM1 G-allele carriers (G:G or G:A) and A-allele homozygous individuals in each of the three participant groups separately.

239 The FLAME model in healthy controls showed significantly higher MID win>neutral anticipation BOLD contrast in G-allele carriers compared with A-allele homozygous individuals at z>3.1. (Figure 5.15.). There were no significant results from the FLAME models in alcohol dependent or gambling disorder participants.

HC OPRM1 G>AA

z 41 z 28 Figure 5.15. – Higher win>neutral anticipation BOLD contrast in G-allele carriers compared with A-allele Z-30 homozygous individuals in 26 healthy Z-1 controls: Violet colour indicates clusters Z28 significant at cluster corrected z>3.1 (3 Z41 clusters: 341, 318 and 224 voxels: – right cerebellum and bilateral parietal white matter). z -1 z -30

Green colour represents results at cluster corrected z>2.3 (6 clusters: 6447, 4569, 3746, 1592, 1489 and 638 voxels – including bilateral dorsal striatum, frontal lobe, insula and cerebellum).

Z Value 0 1 2 3 4

5.3.16. Examining the potential confounding effects of the OPRM1 genotype on

11 correlations between [ C]carfentanil BPND/∆BPND and MID win>neutral anticipation BOLD contrast – FSL FLAME analyses

11 As the OPRM1 polymorphism was associated with both differences in [ C]carfentanil BPND (see Chapter 3, Section 3.3.12.) and MID win>neutral anticipation BOLD contrast (see Section 5.3.15.) further analyses were carried out to examine if the OPRM1 genotype may be confounding any of the significant associations between MID win>neutral anticipation BOLD

240 11 contrast and [ C]carfentanil BPND or ∆BPND in our participants in Section 5.3.7. and Section 5.3.11..

The FSL FLAME models showing significant correlations between MID win>neutral anticipation BOLD contrast and putamen BPND in alcohol dependent participants, and ventral pallidum ∆BPND in gambling disorder participants were rerun with the addition of OPRM1 genotype as a covariate in the model. The addition of OPRM1 genotype as a covariate in these two models did not substantially alter the significant correlations between MID win>neutral

11 anticipation BOLD contrast and [ C]carfentanil BPND or ∆BPND as shown in Sections 5.3.7. and 5.3.11..

5.4. Discussion

In this chapter we did not show significant differences in MID win>neutral anticipation BOLD contrast between our three participant groups.

In alcohol dependent participants lower putamen MOR availability was associated with higher MID win>neutral anticipation BOLD contrast. Significant associations between MOR availability and MID win>neutral anticipation BOLD contrast were not observed in healthy controls or gambling disorder participants. However, there was a trend result in gambling disorder participants of lower ventral pallidum MOR availability associated with higher MID win>neutral anticipation BOLD contrast. The correlation between putamen MOR availability and MID win>neutral anticipation BOLD contrast was significantly different compared between alcohol dependent and gambling disorder participants.

Lower oral dexamphetamine-induced endogenous opioid release in the ventral pallidum was associated with higher MID win>neutral anticipation BOLD contrast in gambling disorder participants. There was also a trend in healthy controls of lower ventral pallidum oral dexamphetamine-induced endogenous opioid release associated with higher MID win>neutral anticipation BOLD contrast. There were no associations observed in alcohol dependent participants between endogenous opioid release and MID win>neutral

241 anticipation BOLD contrast. The correlation between ventral pallidum oral dexamphetamine- induced endogenous opioid release and MID win>neutral anticipation BOLD contrast was significantly different compared between alcohol dependent and gambling disorder participants.

OPRM1 polymorphism G-allele carrier healthy controls were found to have higher MID win>neutral anticipation BOLD contrast compared with A-allele homozygous individuals. The addition of the OPRM1 polymorphism to analyses examining correlations between MID win>neutral anticipation BOLD contrast and putamen MOR availability in alcohol dependent participants, and ventral pallidum endogenous opioid release in gambling disorder participants did not show any substantial changes to the significant correlations shown previously.

5.4.1. MID win>neutral anticipation BOLD contrast compared between groups

In the fMRI dataset there were no significant differences in MID win>neutral anticipation BOLD contrast between groups (healthy control, alcohol dependent or gambling disorder) in the four selected fROIs examined. There was, however, a trend towards lowest win>neutral anticipation BOLD contrast in alcohol dependent participants, and highest win>neutral anticipation BOLD contrast in gambling disorder participants. Exploratory whole-brain FLAME analyses also did not show any significant differences in MID win>neutral anticipation BOLD contrast compared between the three participant groups.

We hypothesised that differences in the salience of the MID financial reward would lead to lowest MID win>neutral anticipation BOLD contrast in alcohol dependent participants and highest contrast in gambling disorder participants. Higher ventral striatum (NAcc) MID win>neutral anticipation BOLD contrast is associated with larger financial reward amounts (Knutson et al, 2001) suggesting that the incentive value or salience of the reward mediates the MID win>neutral anticipation BOLD contrast. However, we did not observe any significant differences in ventral striatum (e.g. NAcc) MID win>neutral anticipation BOLD contrast in either alcohol dependent or gambling disorder participants.

242

In contrast to our findings of no significant differences in win>neutral anticipation BOLD contrast between healthy controls and abstinent alcohol dependent participants, a number of published studies (Beck et al, 2009; Hägele et al, 2015; Romanczuk-Seiferth et al, 2015; Wrase et al, 2007) have shown lower win>neutral anticipation BOLD contrast in alcohol dependence (details of these studies can be found in Section 1.8.). The differences in results could be due to differences in duration of abstinence from alcohol. Most studies showing blunted ventral striatum win>neutral anticipation BOLD contrast in alcohol dependence examined individuals during early abstinence (Beck et al, 2009; Bjork et al, 2008, 2012; Hägele et al, 2015; Romanczuk-Seiferth et al, 2015; Wrase et al, 2007). Two published studies from the ICCAM cohort, which examined alcohol dependent individuals with similar longer durations of abstinence to our participants, did not show significant differences in ventral striatum win>neutral anticipation BOLD contrast (Murphy et al, 2017; Nestor et al, 2017). However, Nestor et al. did show significantly blunted inferior frontal gyrus (IFG)/insula win>neutral anticipation BOLD contrast in alcohol dependent participants. As will be discussed in more detail in Section 5.4.11. it is also possible that with a relatively small sample of 13 alcohol dependent participants we are underpowered to detect differences in win>neutral anticipation BOLD contrast.

Blunted MID win>neutral anticipation BOLD contrast has been shown in gambling disorder participants (Choi et al, 2012; Tsurumi et al, 2014) who, similarly to our gambling disorder cohort, are in current treatment and have comparable durations of abstinence. Therefore, it is less likely that abstinence or treatment status is a reason for the differences in our MID win>neutral anticipation results in gambling disorder compared with other published studies. Other factors such as higher levels of substance dependence comorbidity in the gambling disorder participants in some other studies (Balodis et al, 2012), or differences in task design may have an impact on the differences in MID win>neutral anticipation contrast in gambling disorder. For example, Tsurumi et al. (2014) used ‘points’ as a reward rather than money, and this may have an impact of the salience of the reward in the task.

243 Only one previous study has compared alcohol dependent and gambling disorder participants’ MID task responses, and did not show any significant differences in win>neutral anticipation BOLD contrast (Romanczuk-Seiferth et al, 2015).

It has been shown that higher financial rewards in the MID task evoke a higher win>neutral anticipation contrast (Knutson et al, 2001). The ICCAM fMRI task used financial rewards of £0.50 per trial whilst some published studies showing significant differences in win anticipation BOLD between healthy controls and either alcohol dependent or gambling disorder participants used larger financial rewards per trial (up to €5 or $5 – equivalent to £4 to £4.50 at time of writing) (Balodis et al, 2012; van Holst et al, 2014; Wrase et al, 2007). With larger financial rewards, and an associated higher win>neutral BOLD contrast in healthy controls, any blunting of responses in alcohol dependence may be more pronounced compared with the lack of significant results in this chapter. Furthermore, when gambling, individuals typically win or lose larger amounts of money than £0.50. For example with fixed odds betting terminals an individual can bet up to £100 every 20 seconds (Davies, 2017). Therefore, a larger amount of money at stake, compared with the £0.50 win/loss in the ICCAM MID task, may be associated with an even greater win anticipation BOLD response in gambling disorder participants.

5.4.2. Negative correlations between MID win>neutral anticipation BOLD contrast and putamen MOR availability in alcohol dependence

In the ROI analyses there was evidence of negative correlations between MID win>neutral anticipation BOLD responses and putamen and ventral pallidum MOR availability in alcohol dependent and gambling disorder participants respectively, but no correlations in healthy controls. In alcohol dependent participants the negative correlation was present in FLAME whole-brain analyses where lower putamen MOR availability was associated with higher win>neutral anticipation BOLD contrast in the post- and pre-central gyri bilaterally. This result was not in keeping with our hypothesis of higher MOR availability associated with higher win>neutral anticipation BOLD responses.

244 We hypothesised that higher MOR availability may represent a higher sensitivity of endogenous opioid signalling and resulting in higher mesocorticolimbic dopaminergic activity (see Section 1.3.2.). There is evidence that in the striatum higher [11C]raclopride (dopamine

11 D2/3 receptor) binding is associated with higher [ C]carfentanil binding in healthy controls (Tuominen et al, 2015) but the association between MOR availability and striatal dopamine release has not previously been examined in humans. Furthermore, we did not examine MOR availability in the VTA, where MOR signalling has been shown to increase dopaminergic activity (Spanagel et al, 1992), but in other regions such as the striatum and ventral pallidum where the associations between MOR agonism and dopaminergic signalling is less well characterised. It is possible that MOR agonism in the striatum or ventral pallidum has an inhibitory effect on dopaminergic signalling. For example, one study has shown a potential inhibitory role of MORs in the striatum on dopamine release (Pjzntney and Gratton, 1991).

Whilst there is evidence that the MID task induces striatal dopamine release (Schott et al, 2008), similar studies investigating activation of endogenous opioid signalling during the MID task have not been published. There is, however, evidence that the MOR antagonist naltrexone does not modulate MID win>neutral anticipation BOLD contrast in healthy controls (Nestor et al, 2017), suggesting there is no significant MOR signalling during the MID task. The lack of an association between MOR availability and MID win>neutral anticipation BOLD contrast in our healthy controls may also provide further evidence that MORs do not have a modulatory effect on MID win anticipation responses.

Our results did show higher MOR availability associated with lower MID win>neutral anticipation BOLD contrast in alcohol dependent participants. This association is in the opposite direction to our hypothesis and may suggest that in addiction higher MOR availability is associated with lower MID reward anticipation and possibly an associated lower MID task-related dopamine release.

It is possible that factors such as clinical variables associated with addiction or recovery may be mediating MOR availability and MID win>neutral anticipation BOLD responses and resulting in the negative correlations we observed in alcohol dependent participants. In alcohol dependence during early abstinence it has been shown that individuals with higher

245 MOR availability and lower MID win>neutral anticipation BOLD contrast report higher craving (Heinz et al, 2005; Williams et al, 2009; Wrase et al, 2007). However, our stable long-term abstinent alcohol dependent participants did not report craving suggesting that craving may not be an adequate explanation for our associations between MOR availability and win>neutral anticipation BOLD.

Higher MOR availability is also associated with a higher risk of relapse in cocaine dependence (Ghitza et al, 2010; Gorelick et al, 2008), although this has not been similarly examined in alcohol dependence. In the ICCAM cohort alcohol dependent participants, who have comparable long-term abstinence to our participants, lower MID win>neutral anticipation BOLD contrast is associated with a higher risk of relapse (Paterson et al, 2015, 2017). This low MID win>neutral anticipation BOLD contrast may represent low dopamine function, and it has been shown that low oral methylphenidate induced dopamine release is associated with higher risk relapse risk in methamphetamine dependence (Wang et al, 2012a). Our alcohol dependent participants with high MOR availability and low MID win>neutral anticipation BOLD contrast may be at higher risk for relapse, unfortunately, there was no follow-up data examining relapse in our participants, and therefore it is not possible to explore the associations between our findings and relapse risk.

Impulsivity is another possible clinical variable that may link MOR availability and MID win>neutral anticipation BOLD contrast. In gambling disorder both higher MOR availability and lower MID win>neutral anticipation BOLD contrast have been associated with higher UPPS-P negative urgency and BIS motor subscale respectively (Balodis et al, 2012; Mick et al, 2016). In alcohol dependence higher BIS-10 impulsivity is associated with lower MID win>neutral anticipation BOLD contrast (Beck et al, 2009), however, there are no similar associations between MOR receptor availability and impulsivity in alcohol dependence as shown in gambling disorder (Chapter 3, Section 3.3.8.).

246 5.4.3. Putamen MOR signalling in alcohol dependence

MID win>neutral anticipation BOLD contrast was significantly correlated with MOR availability in the putamen in alcohol dependent participants. Compared with the ventral pallidum and NAcc where MOR signalling plays an important role in reward including mediation of reward ‘liking’ and ‘wanting’ (Berridge and Kringelbach, 2015; Smith et al, 2009), the role of MOR in the putamen is less well characterised.

MOR in the dorsal striatum, which includes both putamen and caudate, are concentrated in ‘patch compartments’ (Banghart et al, 2015). Neurons from these patch compartments project to dopaminergic neurons in the VTA and substantia nigra and are hypothesised to play an important role in reward expectation (Watabe-Uchida et al, 2012). The dorsal striatum is also proposed to play an important role in mediating habitual behaviours and the development of incentive salience in addiction (Everitt and Robbins, 2013; Koob and Volkow, 2016; Volkow et al, 2006).

The putamen may also play an important role in cue mediated relapse to heavy drinking. Alcohol cue reactivity, measured with fMRI, in the putamen increases during early abstinence and this ‘incubation’ of cue responses during the first 2 weeks of abstinence is associated with a higher risk of relapse to heavy drinking (Bach et al, 2019). Naltrexone treatment blunts this increase in alcohol cue responses and reduces relapse risk, and Bach et al, (2019) hypothesise that this reflects the important role of opioidergic signalling in modulating the dorsal striatum mediated habitual behaviours associated with addiction. Further work examining putamen MOR availability and alcohol cue responses may help to better understand the links between cue reactivity, opioid receptor antagonists and relapse in alcohol dependence.

247 5.4.4. Negative correlations between MID win>neutral anticipation BOLD contrast and oral dexamphetamine-induced endogenous opioid release in gambling disorder participants

In gambling disorder higher oral dexamphetamine-induced ventral pallidum endogenous opioid release is associated with lower MID win>neutral anticipation BOLD contrast in the frontal pole. MOR signalling in the ventral pallidum plays an important role in reward including mediation of reward ‘liking’ and ‘wanting’ (i.e. valence and salience) (Berridge and Kringelbach, 2015; Smith et al, 2009). It is possible that in gambling disorder, where the MID financial rewards are salient, the ventral pallium plays an important role in assigning the hedonic value or valence of the MID reward and the subsequent motivation to obtain this reward.

We proposed in Section 4.4.3. that the ‘low salience’ of dexamphetamine in alcohol dependence may be mediating the blunted oral dexamphetamine-induced endogenous opioid release. Mick et al. (2016) also suggest that the low salience of dexamphetamine in gambling disorder may have also be a mechanism of the blunted endogenous opioid release. In contrast to dexamphetamine, winning money in the MID task may be a ‘salient’ reward in gambling disorder, although we did not find significantly higher MID win>neutral anticipation BOLD contrast in gambling disorder participants. Limbrick-Oldfield et al. (2017) examined BOLD responses to gambling cues in 19 of the 20 gambling disorder participants included in this thesis (Chapter 2, Sections 2.1.4 and 2.1.5.), and found higher BOLD responses to gambling cues in gambling disorder participants compared with controls (Limbrick-Oldfield et al, 2017). This is similar to the higher BOLD responses to salient alcohol cues shown in alcohol dependence (Myrick et al, 2004; Schacht et al, 2013a). There may be divergent responses to salient and non-salient rewards or cues in addiction (Koob and Volkow, 2016; Lubman et al, 2008), with ‘hyperactive’ responses to salient addiction-related cues and blunted responses to non-addiction-related cues or rewards. This divergence in salient and non-salient responses in addiction is a potential mechanism for the negative correlation between endogenous opioid release following a ‘non-salient’ dexamphetamine challenge and the ‘salient’ financial reward in the MID task in our gambling disorder participants. Further research examining the associations between gambling cue BOLD responses and oral

248 dexamphetamine-induced endogenous opioid release in our gambling disorder participants may confirm this.

Another possible mechanism for our negative correlations between MID win>neutral anticipation BOLD contrast and endogenous opioid release is an association between endogenous opioidergic and dopaminergic tone in individuals with gambling disorder. As discussed previously higher win>neutral anticipation BOLD responses are associated with higher MID task associated dopamine release (Schott et al, 2008). Therefore, it is possible that our negative correlation between MID win>neutral anticipation BOLD contrast and dexamphetamine induced endogenous opioid release reflects a negative correlation between dopaminergic and opioidergic tone in gambling disorder. PET studies have shown blunted dexamphetamine induced endogenous opioid release (Mick et al, 2016) and enhanced dexamphetamine induced dopamine release (Boileau et al, 2014) in gambling disorder participants compared with healthy controls. However, no studies have examined both dopamine and endogenous opioid release in the same gambling disorder participants, or indeed any other participant group. Therefore, further research is required to better understand the relationship between endogenous opioid and dopamine tone, and if this may an underlying mechanism for our associations between MID win>neutral anticipation BOLD contrast and endogenous opioid release.

It is also important to note that oral dexamphetamine-induced endogenous opioid release, whilst an indication of endogenous opioid tone, is not an indication of endogenous opioid release during the MID task. The [11C]carfentanil PET and fMRI imaging in this study were carried out sequentially on separate study visits and therefore changes in endogenous opioid concentrations following the oral dexamphetamine challenge would not affect endogenous opioid modulation of the MID task scan. Furthermore, endogenous opioid release during the behavioural MID task may not be comparable with endogenous opioid release following the oral dexamphetamine challenge.

249 5.4.5. Associations between oral dexamphetamine-induced endogenous opioid release and MID win>neutral anticipation BOLD contrast – Comparisons between alcohol dependence and gambling disorder

There was evidence in Section 5.3.12. of significant differences in the correlation between dexamphetamine-induced ventral pallidum endogenous opioid release and MID win>neutral anticipation BOLD contrast in alcohol dependent participants compared gambling disorder participants. There is also a suggestion in Figure 5.13. that whilst the correlation is positive in gambling disorder participants (Pearson’s R=0.69, p=0.008) it is negative in alcohol dependent participants (Pearson’s R=-0.58, p=0.022). However, this result is difficult to interpret as there were no significant correlations (negative or positive) between endogenous opioid release and MID win>neutral anticipation BOLD contrast observed in ROI or FLAME analyses in alcohol dependent participants.

A difference in the direction of the association of endogenous opioid release and MID win>neutral anticipation BOLD contrast between alcohol dependent and gambling disorder participants could be due to differences in the salience of the MID financial reward in these two participant groups. As discussed above in Section 5.4.4. the negative correlation in gambling disorder may be due to a divergent response to salient and non-salient stimuli in addiction. In alcohol dependence if both the MID financial reward (Balodis and Potenza, 2015; Murphy et al, 2017; Nestor et al, 2017) and dexamphetamine challenge are non-salient, it may be expected that both responses will be similarly blunted in an individual. Although there was a suggestion of a non-significant trend towards higher MID win>neutral anticipation BOLD contrast in our gambling disorder participants, there were no significant results that might confirm differences in salience of the MID financial reward compared between alcohol dependent and gambling disorder participants in our dataset.

One possible mechanism for the effect of salience on win>neutral anticipation BOLD contrast is an activation of endogenous opioid signalling during salient rewards or stimuli. There is fMRI evidence that higher striatal BOLD responses associated with alcohol cues in alcohol dependent individuals are lowered following administration of opioid receptor antagonist naltrexone (Myrick et al, 2008; Schacht et al, 2017). Furthermore, whilst naltrexone does not

250 modulate blunted ICCAM MID win>neutral anticipation BOLD contrast in alcohol dependent individuals (Nestor et al, 2017), when the ICCAM MID task is accompanied by an intravenous alcohol infusion (i.e. a salient challenge in alcohol dependence), the opioid receptor antagonist nalmefene lowers win>neutral anticipation BOLD contrast (Quelch et al, 2017). Activation of endogenous opioid signalling in gambling disorder participants during the ‘salient’ MID task financial reward may be a mechanism for the correlation between win>neutral anticipation BOLD contrast and dexamphetamine induced endogenous opioid release observed in gambling disorder but not alcohol dependence where the MID rewards are ‘non-salient’. To investigate this hypothesis further, research examining the effect of opioid antagonists on BOLD responses to salient rewards in gambling disorder similar to the literature discussed above in alcohol dependence is required. Furthermore, PET studies are required to examine endogenous opioid release during the MID task, similarly to the study by Schott et al. (2008) examining dopamine release with [11C]raclopride PET during a modified MID task.

The mechanism by which salience might modulate endogenous opioid signalling is not well characterised. Glutamatergic projections from frontal to striatal regions are proposed to mediate the salience of responses to stimuli, and there is extensive evidence of dysfunction in this network in addiction (Britt et al, 2012; Geisler and Wise, 2008; Kalivas, 2009; Koob and Volkow, 2016). These glutamatergic projections, particularly to the VTA and NAcc (Britt et al, 2012; Geisler and Wise, 2008), may be a mechanism for salience mediated responses to addiction-related cues. For example, cue-related reinstatement of heroin self-administration in rats increases glutamate in the NAcc, and can be prevented by AMPA/kainate glutamate blockade in the NAcc (LaLumiere and Kalivas, 2008). There is evidence of interactions between glutamatergic and opioidergic signalling (Chartoff and Connery, 2014; Krystal et al, 2007), but the mechanism by which glutamatergic signalling may mediate salience-related endogenous opioid release is unclear.

Another possible mechanism for the difference in the correlation between endogenous opioid release and MID win>neutral anticipation BOLD contrast in alcohol dependent participants compared with gambling disorder participants may be due to differences in the associations between endogenous opioidergic and dopaminergic tone in these two

251 addictions. Both alcohol dependence and gambling disorder are associated blunted with dexamphetamine endogenous opioid release (Mick et al, 2016), however in alcohol dependence dexamphetamine induced dopamine release is blunted (Martinez et al, 2005) in contrast to gambling disorder where it is enhanced (Boileau et al, 2014). Unfortunately, as discussed above in Section 5.4.4. there are no published studies examining the associations between endogenous opioid and dopamine release in either gambling disorder or alcohol dependence.

5.4.6. The potential confounding effects of differences in the MID task behavioural measures between groups on win>neutral anticipation BOLD contrast

There was evidence of poorer MID task performance (i.e. accuracy and reaction time) in healthy controls compared with gambling disorder participants. This was significant in the larger fMRI dataset and remained as a trend in the smaller combined PET and fMRI dataset. Poorer task performance may reflect lower engagement or motivation in the MID task and could have an impact on brain responses during the anticipation period.

However, there was no evidence of significant associations between task behaviour and MID win>neutral anticipation BOLD contrast in our healthy controls. Knutson et al. (2001) showed that larger financial rewards were associated with higher ventral striatal win>neutral anticipation BOLD contrast, but this was not associated with any differences in task accuracy or reaction times (Knutson et al, 2001). The lack of associations between task behavioural measures and MID win>neutral anticipation BOLD contrast in our data suggests that differences in task performance between groups may not be a confounding factor affecting differences in win>neutral anticipation BOLD contrast.

252 5.4.7. The OPRM1 polymorphism and MID win>neutral anticipation BOLD contrast in healthy controls

The OPRM1 A118G polymorphism G-allele was associated with higher MID win>neutral anticipation BOLD contrast in healthy controls, but not in alcohol dependent or gambling disorder participants. It is possible that the OPRM1 genotype has less of a modulatory effect on mesocorticolimbic responses during MID task win anticipation in alcohol and gambling disorder due to the dysregulation of opioid signalling in these groups. It is also likely that the number of G-allele carriers in our sample is too small to adequately assess the influence of the OPRM1 polymorphism in our participants (see Section 5.4.9.).

The effects of the OPRM1 polymorphism on MID win>neutral BOLD contrast have not been previously reported, although other studies have examined the effects of other

66 polymorphisms including the Brain-derived neurotrophic factor (BDNF) Val Met and DRD2 polymorphisms (Peciña et al, 2013, 2014; Richter et al, 2017). One study has shown that the OPRM1 G-allele is associated with higher striatal dopamine release following an alcohol challenge (Ramchandani et al, 2011). This may represent a higher dopaminergic responsiveness in G-allele carriers and a possible explanation for higher MID win>neutral anticipation BOLD contrast in our G-allele carrier healthy controls. fMRI studies examining the effect of the OPRM1 A118G polymorphism on BOLD responses to an alcohol taste challenge have shown mixed results with higher and lower responses in G- allele carriers or no differences associated with the OPRM1 polymorphism (Filbey et al, 2008; Korucuoglu et al, 2017; Ziauddeen et al, 2016). This lack of consistency may be due to differences in the task, for example the use of water or juice as the control condition, and participant populations between the studies, for example overweight participants in one study (Ziauddeen et al, 2016), and heavy drinkers in another (Filbey et al, 2008).

253 5.4.8. The potential mediating effects of the OPRM1 polymorphism on associations between MID win>neutral anticipation BOLD contrast and [11C]carfentanil

PET measures (BPND/∆BPND)

Given the association of both the OPRM1 G-allele with higher MID win>neutral anticipation BOLD contrast and lower MOR availability, in both our participants and other published cohorts (Domino et al, 2015; Nuechterlein et al, 2016; Peciña et al, 2015b; Ray et al, 2011; Weerts et al, 2013), it is possible that OPMR1 genotype may be a mediating or confounding factor in the associations between MID win>neutral anticipation BOLD contrast and MOR availability or endogenous opioid release. The addition of OPRM1 genotype as a covariate in our FSL FLAME models examining associations between MID win>neutral anticipation BOLD

11 contrast and [ C]carfentanil BPND/∆BPND did not eliminate the significant correlations observed in alcohol dependent and gambling dependent participants. This suggests that the significant correlations we observed in alcohol dependent and gambling disorder participants may not be due to the confounding effects of OPRM1 genotype on MID win>neutral

11 anticipation BOLD contrast and [ C]carfentanil BPND/∆BPND. However, the numbers of G- allele carriers in our dataset is very small, particularly in the combined [11C]carfentanil PET and fMRI dataset with only four G-allele carriers in each group, and therefore it may not be possible to draw firm conclusions form our results.

11 Given the evidence of an effect of the OPRM1 polymorphism on both [ C]carfentanil BPND (Domino et al, 2015; Nuechterlein et al, 2016; Peciña et al, 2015b; Ray et al, 2011; Weerts et al, 2013) and our MID win>neutral anticipation BOLD contrast it would be of interest to investigate if there is an interaction effect of OPRM1 polymorphism on associations between

11 [ C]carfentanil BPND and MID task BOLD responses. This would require larger sample sizes than those used in our analysis and may require active selection of OPRM1 G-allele carriers to ensure an adequate sample of these individuals. One potential selection method used by a number of studies is pre-screening participants for the OPRM1 genotype allowing equal numbers of G-allele carrier and A-allele homozygous participants to be recruited (Ramchandani et al, 2011; Ray et al, 2013).

254 Previous published studies examining the associations between [11C]carfentanil PET measures and fMRI BOLD responses did not examine the effect of OPRM1 polymorphism on their results (Karjalainen et al, 2017; Rabiner et al, 2011; Schrepf et al, 2016). It is important to understand

11 if OPRM1 genotype has an interaction effect on association between [ C]carfentanil BPND and the fMRI task responses to better interpret these results.

5.4.9. Limitations

Sample Sizes One important limitation of the analyses in this chapter is the small sample sizes. As detailed in Section 2.16., the clinical studies these data were collected in were designed and powered for the [11C]carfentanil PET outcome measures. The fMRI and combined PET/fMRI analyses were exploratory, and the studies were not powered for these analyses.

The MID fMRI sample consisted of 27 healthy controls, 13 alcohol dependent and 20 gambling disorder participants. The ICCAM platform study, from which our MID task originated, was designed to be adequately powered with 20 individuals in each group (Paterson et al, 2015). Published analyses from the ICCAM platform comparing MID win anticipation BOLD signal between groups have used 20 to 35 participants in each group and only show differences in win>neutral anticipation BOLD contrast between healthy controls and alcohol dependent participants in the right inferior gyrus (Murphy et al, 2017; Nestor et al, 2017). Other studies examining differences in MID win anticipation responses between healthy controls and alcohol dependent participants in early abstinence used samples sizes ranging from 16 to 29 in each group, with most studies having at least 20 in each group (Beck et al, 2009; Bjork et al, 2012; Wrase et al, 2007). The published studies examining differences in MID win anticipation in gambling disorder compared with healthy controls tended to have lower sample sizes, two with 13 to 15 in each group (Balodis et al, 2012; Choi et al, 2012), but one had higher numbers with 24 and 27 in each group (Tsurumi et al, 2014). Whilst our numbers of gambling disorder participants and healthy controls are within the range of other studies, the number of alcohol dependent participants is low by comparison and this strongly suggests

255 our analyses were underpowered to adequately detect differences in this group compared with the others.

There are fewer published studies to compare sample sizes with our combined PET and fMRI analyses where there were 13 healthy controls, 13 alcohol dependent and 15 gambling disorder participants. No studies have examined associations between [11C]carfentanil PET and the MID task BOLD responses, but two published studies used [11C]carfentanil PET to examine associations between MOR availability and fMRI measures related to pain recruited 33 healthy controls (Karjalainen et al, 2017) and 18 fibromyalgia patients (Schrepf et al, 2016). No studies have examined associations between endogenous opioid release using [11C]carfentanil PET and fMRI measures, but a study examining associations between MOR occupancy with opioid receptor antagonists naltrexone and GSK1521498 using [11C]carfentanil and associated fMRI measures in healthy controls had 26 participants (Rabiner et al, 2011).

Published studies examining the associations between MID BOLD responses and dopamine receptor PET typically included lower numbers of participants compared with combined [11C]carfentanil PET and fMRI studies. For example, Heinz et al. (2004) recruited 13 healthy controls and 11 alcohol dependent participants to compare associations between MID win>neutral anticipation BOLD contrast and dopamine D2 receptor availability ([18F]desmethoxyfallypride) (Heinz et al, 2004). A study examining associations between endogenous dopamine release using [11C]raclopride and MID win>neutral anticipation BOLD contrast had 11 healthy controls (Schott et al, 2008).

To further examine if our combined PET and fMRI analyses were adequately powered, post- hoc power calculations were carried out using the Pearson’s correlations results from our significant ROI analysis correlations (Sections 5.3.6. and 5.3.10.). For the significant

11 correlations between [ C]carfentanil BPND (MOR availability) and MID win anticipation %BOLD signal change there was a range of required sample sizes from 14 for the putamen

BPND and putamen %BOLD signal change correlation (R=-0.688) in alcohol dependent participants to 24 for the ventral pallidum BPND and putamen %BOLD signal change correlation (R=-0.559) in gambling disorder participants. For the significant correlations

256 11 between ventral pallidum [ C]carfentanil ∆BPND (endogenous opioid release) and MID win anticipation %BOLD signal change in healthy controls there was a range of required sample sizes from 13 for the ventral pallidum ∆BPND and caudate %BOLD correlation (R=-0.724) to 18 for the ventral pallidum ∆BPND and putamen %BOLD correlation (R=-0.624).

These power calculations suggest that despite finding significant results in our dataset, a minimum of 20-25 participants in each group is likely to be required to be adequately powered to detect correlations between [11C]carfentanil PET and MID win>neutral anticipation BOLD contrast. Therefore, our analyses were underpowered. As discussed in Section 2.15. underpowered analyses are at risk of false positive (i.e. type 1 error) as well as false negative (i.e. type 2 error) results (Button et al, 2013), and therefore the results presented in this chapter should be interpreted with caution. However, given the unique nature of the dataset, combining [11C]carfentanil PET and fMRI measures in both alcohol dependence and gambling disorder, it is still of interest to examine these associations to help better understand the link between dysregulated opioid signalling and reward in addiction.

As discussed in Section 5.4.8. The numbers of G-allele carriers in each group was very small, and therefore the analyses investigate the effect of the OPRM1 A118G G-allele on the associations between MID BOLD responses and [11C]carfentanil PET are not adequately powered. Potential methods to adequately power further investigations of whether the OPRM1 genotype mediates or confounds associations between [11C]carfentanil PET and fMRI measures are also discussed in Section 5.4.7..

Group demographics There were some demographic variables that were not matched between the groups; primarily older age and more current smokers in the alcohol dependent group compared with the other two participant groups. Older age has been shown to be associated with higher MOR availability in the putamen in healthy controls (see Section 3.3.11.), and there is also an effect of age on fMRI BOLD signal variability (Garrett et al, 2017). MID win anticipation BOLD responses may be blunted in current smokers (Rose et al, 2013), although we did not observe any differences in MOR availability or oral dexamphetamine-induced endogenous opioid

257 release between current smokers and non-smokers in our healthy controls or alcohol dependent participants (Chapters 3 and 4).

These variables were, however, not controlled for in the FSL FEAT analyses. This is primarily due to the small sample sizes used in these analyses, particularly in the combined PET/MID fMRI models, and the risk of overfitting the model with the addition of two further covariates. It is recommended that there are a minimum of 10 to 15 observations (i.e. participants) per variable of interest in regression type models to avoid overfitting (Babyak, 2004). Therefore

11 a FLAME model including [ C]carfentanil BPND, age and smoking status (i.e. three variables of interest) would require a minimum sample size of 30 to 45 individuals to avoid overfitting the model, substantially larger than our sample sizes of 13 to 15. Further analysis with larger sample sizes are required to better understand the influence of age and smoking status on the associations between win>neutral anticipation BOLD contrast and [11C]carfentanil

BPND/∆BPND.

Depression and depressive symptoms Previous history of depression and current depressive symptoms are also potential confounding variables in our analyses. Although there were no participants with current depression in our dataset, both alcohol dependent and gambling disorder groups included participants with past histories of depression. Furthermore, the gambling disorder group had significantly higher BDI scores than either healthy controls or alcohol dependent participants (see Sections 5.3.1. and 5.3.4.), suggesting a greater prevalence of depressive symptoms in this group.

There is evidence of changes in MID win>neutral BOLD contrast associated with current major depressive disorder including blunting in the ventral striatum (Stoy et al, 2012) and higher contrast in cortical regions including the anterior cingulate (Knutson et al, 2008) compared with controls. Our participants do not have current major depression, and Stoy et al, (2011) showed that the blunted ventral striatum win>neutral BOLD contrast resolved following treatment with escitalopram, although it is difficult to interpret if this is due to improving mood or a direct effect of antidepressant medication. One study examined individuals with

258 major depressive disorder in remission, which may more closely reflect our alcohol dependent and gambling disorder participants with previous depression. This study showed greater MID win>neutral BOLD contrast in the anterior cingulate, mid fontal gyrus and right cerebellum in the major disorder in remission individuals compared with controls (Dichter et al, 2012). Additionally, adolescents without clinical depression but with depressive symptoms (classified as BDI scores >10) also show higher MID win>neutral BOLD contrast in the middle frontal gyrus and parietal lobe (Mori et al, 2016) compared with controls. This may also be relevant to our gambling disorder participants with significantly higher mean BDI scores than the other participant groups, albeit the mean BDI scores were not above the >10 threshold used in the Mori et al, (2016) study.

It is possible that both previous depression history and higher depressive symptoms had a confounding effect on our comparisons of MID win>neutral BOLD contrast between the three groups. Furthermore, it is possible that depression may also have in impact on associations between MID win>neutral BOLD contrast and [11C]carfentanil PET measures, particularly endogenous opioid release which could also be blunted in individuals with depression (see Sections 3.3.9. and 4.4.10. for further discussions regarding the impact of depression and depressive symptoms on MOR availability and endogenous opioid release). Further research specifically investigating the impact of current or past depression on associations between MID win>neutral BOLD contrast and [11C]carfentanil PET measures would help to better understand this issue. However, it may be difficult to find alcohol dependent or gambling disorder participants without a history of depression or depressive symptoms as depression is a very common comorbidity in both substance and behavioural addictions (Brook et al, 2002; Lorains et al, 2011).

The temporal associations between addiction and depression are complex. For example, dependent alcohol use predicts the development of depressive symptoms within 1-year, as well as depressive symptoms predicting the development of alcohol dependence (Gilman and Abraham, 2001), and it is likely that some depressive symptoms in alcohol dependent individuals are due to the depressive effects of heavy alcohol consumption (Hasin et al, 1996). Alcohol withdrawal is also associated with an increase in depressive symptoms which typically improve within 3-4 weeks of abstinence (Liappas et al, 2002). The associations between

259 gambling disorder and depression is also similarly complex (Kim et al, 2006). Therefore, there is an added dimension of complexity when interpreting a previous history of depression or depression diagnosis in an individual with addiction as depressive symptoms may be a discrete depressive disorder, a result of an addictive disorder or a combination of the two. Furthermore, the intertwined nature of addiction and depression may make any future research attempting to investigate the interaction between the effects of depression and addiction on our [11C]carfentanil PET and MID task BOLD measures complex to interpret.

Non-simultaneous [11C]carfentanil PET and MID task fMRI scans Our PET and fMRI scans were not carried out simultaneously but were carried out on separate study visits, typically 2 to 4 weeks apart. [11C]carfentanil PET scans were carried out on a separate visit to the MRI scan to prevent any fMRI task associated endogenous opioid release

11 altering [ C]carfentanil BPND and ∆BPND. However, this raises the possibility of changes in MOR availability in the time between the two visits.

The only published test-retest [11C]carfentanil study had both test and retest scans on the same day (Hirvonen et al, 2009), and so does not provide detail on the longer term (i.e. 2 to

11 4 weeks) variability in [ C]carfentanil BPND. There is evidence that the non-specific opioid receptor ligand [11C]diprenorphine shows low test-retest variability over a mean of 55 days (Hammers et al, 2007) suggesting that opioid receptor availability may be stable over the period of time between our fMRI and PET scan visits, although further evidence is required to confirm this for [11C]carfentanil.

Ventral Pallidum ROI The ventral pallidum ROI used in this analysis, whilst anatomically accurate, is small and directly abutting the larger ventral striatum/NAcc (see Figure 2.12.). This leads to the

11 likelihood of a partial volume effect where the ‘ventral pallidum’ [ C]carfentanil BPND represents both signal from the anatomical ventral pallidum and ‘spill-in’ signal from the posterior section of the NAcc. It is not possible to quantify the proportional contribution of

11 ventral pallidum or posterior NAcc MOR blinding to the [ C]carfentanil BPND value (see

260 Section 3.4.8. for more details regarding partial volume effects and corrections). Both the ventral pallidum and NAcc show similar effects of opioidergic signalling in modulating hedonic responses to reward in rats (Berridge and Kringelbach, 2015). Therefore, whilst we cannot

11 determine the degree to which our [ C]carfentanil BPND represents ventral pallidum or ‘posterior NAcc’, this may not affect the interpretation of our results that the ventral pallidum ROI represents the endogenous opioid regulation of the hedonic value of the rewards in the MID task.

It is interesting that, whilst the ventral pallidum/posterior NAcc ROI shows significant associations in both BPND and ∆BPND analyses, the NAcc ROI does not. In rodents anatomical- functional substructures (e.g. core and shell) of the NAcc are well described (Zahm, 1999), and there are regionally specific opioidergic ‘hot’ and ‘cold’ spots in the rat NAcc (and ventral pallidum) (Berridge and Kringelbach, 2015). A ‘posterior substructure’ of the NAcc/ventral striatum in humans may be more important in relation to opioidergic modulation of reward, and in a similar manner to the ventral pallidum, than the anterior section of the NAcc. However, little is understood of functional-anatomical substructures in the human NAcc compared to those described in rodents, and further research would be required to elucidate whether opioidergic signalling plays a different role in different sub-regions of the human NAcc.

Cluster correction in FSL FEAT analysis The family wise error (FWE) cluster determining threshold (CDT) primarily used in the FLAME fMRI analysis for this chapter was z>3.1 (FWE p<0.05, CDT p<0.001), rather than the FSL default z>2.3 (FWE p<0.05, CDT p<0.01). Recently it has been shown that a FWE CDT of z>2.3 is associated with a high level of type I errors (false positives) and it is recommended to use a correction of z>3.1, or permutation testing with between 1,000-10,000 permutations (Eklund et al, 2016). A further issue with using the lower z>2.3 correction is a resultant low confidence in the of anatomical specificity of significant clusters in a whole brain analysis (Woo et al, 2014).

261 Due to the large number of exploratory analyses carried out in this chapter results significant at a FWE CDT of z>2.3 were not reported as primary findings due to the risk of these results representing false positives due to multiple comparisons. However, some trend results significant at a FWE CDT of z>2.3 were included in this chapter.

There are a range of methods used to control for multiple comparisons and type I errors in the published literature relevant to our analyses. Of the published ICCAM studies comparing MID task win>neutral BOLD contrast between groups, one used z>2.3 (FWE p<0.05, CDT p<0.01) (Nestor et al, 2017), whilst the other used FWE p<0.05 and 5,000 permutations (Murphy et al, 2017). Other studies comparing MID task win>neutral BOLD contrast between healthy controls and either alcohol dependent or gambling disorder participants do not mention what CDT was used in conjunction with FWE correction (van Holst et al, 2014; Wrase et al, 2007), or used a correction less stringent than CDT p<0.001 (Balodis et al, 2012).

The published studies examining the associations between PET and fMRI measures using whole-brain analysis have also used a range of methods for addressing the issue of multiple comparisons. Karjalainen et al. (2017) had a similar issue with large numbers of multiple comparisons and used FWE p<0.05 with 10,000 permutations, but also included some results using a less stringent threshold of uncorrected p<0.05 and cluster size >1000 voxels (Karjalainen et al, 2017). Other studies used less stringent corrections ranging from FWE z>2.5 (Dubol et al, 2018) to using FWE p<0.05 but a CDT of >10 voxels (Dubol et al, 2018). Other studies did not state if FWE correction was used in their methods (Schott et al, 2008; Schrepf et al, 2016).

BOLD signal assumptions Finally, it should be noted that the fMRI outcome measure in this analysis is MID win>neutral BOLD contrast. Therefore, rather than a measure of brain ‘activation’ during anticipation of receiving a reward, our contrast measures the proportional change in BOLD signal compared with the neutral anticipation. An individual (or group) may have higher responses to the anticipation of winning the MID financial reward, but if their baseline (neutral anticipation) activity is also higher this may result in a low win>neutral anticipation BOLD contrast. It is

262 possible that changes in MOR availability or endogenous opioid tone may be associated with a change in mesocorticolimbic dopaminergic tone affecting both neutral and win anticipation BOLD responses. For example, an oral dexamphetamine challenge blunts striatal MID win>neutral anticipation BOLD contrast (Knutson et al, 2004), despite the likely increase in striatal dopaminergic activity. This may be due to higher ‘activity’ during the baseline neutral anticipation condition as well as the win anticipation condition, leading to a reduced proportional contrast, and a lower win>neutral anticipation BOLD contrast.

Further research, for example examining the effect of MOR agonists in the MID task in humans, or the use of more invasive measures in animals, such as paramagnetic fMRI tracers or electrostimulation (Logothetis, 2003), may provide a better understanding of the impact of MOR signalling on mesocorticolimbic activity during the MID task.

5.5. Conclusion

There were no significant differences in MID win>neutral anticipation BOLD contrast between any of our three participant groups. This was not in keeping with our hypothesis that MID win>neutral anticipation BOLD contrast would be lower in alcohol dependent participants and higher in gambling disorder participants due to possible differences in the salience of a monetary reward in gambling and alcohol addiction.

In alcohol dependence lower putamen MOR availability was associated with higher MID win>neutral anticipation BOLD contrast. There were no significant associations between MOR availability and MID win>neutral anticipation BOLD contrast in healthy controls or gambling disorder participants. Our results in alcohol dependence may reflect an association between dysregulated opioidergic signalling and dopaminergic mesocorticolimbic function. The direction of the association between MOR availability and MID win>neutral anticipation BOLD contrast was the opposite to our hypothesis. It is possible that other clinical factors, such as risk of relapse or impulsivity may be mediating this association rather than a direct effect of

263 MOR signalling on MID win>neutral anticipation BOLD contrast, although further research is required to better understand these associations.

In gambling disorder participants higher MID win>neutral anticipation BOLD contrast was associated with lower dexamphetamine-induced endogenous opioid release. There was also a suggestion that the association between win>neutral anticipation BOLD contrast and dexamphetamine-induced endogenous opioid release was significantly different compared between gambling disorder and alcohol dependent participants. These correlations between MID BOLD responses and endogenous opioid release may reflect the association between opioidergic and dopaminergic tone in gambling disorder. It is also possible that a dysregulation of responses to salient and non-salient stimuli in addiction may be mediating the associations between dexamphetamine-induced endogenous opioid release and MID win>neutral anticipation BOLD contrast in gambling disorder participants.

The analyses and results in this chapter are post-hoc and exploratory, and the interpretation of these results is therefore limited by these constraints. However, the results us a unique dataset to provide an insight into associations between endogenous opioid neurotransmission, reward responses and mesocorticolimbic signalling in substance and behavioural addiction. Furthermore, these results highlight interesting avenues for further research as will be discussed in Section 6.5.

264 CHAPTER 6: GENERAL DISCUSSION

This discussion chapter will briefly outline the aims of the thesis, which can be found in more detail in Chapter 1, Section 10.10., and then the findings of three results chapters, each of which addresses one of the three aims, will be summarised. Following this the implications of the results in this thesis for the treatment of alcohol dependence will be discussed, as well as directions for future work.

Briefly, the aims of this thesis were: 1. To examine MOR availability in alcohol dependence. 2. To examine endogenous opioid tone in alcohol dependence. 3. To examine associations between reward sensitivity, MOR availability and endogenous opioid tone.

6.1. Long-term abstinent alcohol dependent individuals do not have higher MOR availability

It was hypothesised that alcohol dependent participants would have higher MOR availability compared with healthy controls, based on previously published PET literature (Heinz et al, 2005; Weerts et al, 2011; Williams et al, 2009). However, we did not find significant differences in MOR availability between alcohol dependent participants and healthy controls. This may be due to the longer durations of abstinence in our alcohol dependent participants, compared with other studies which used individuals who were recently abstinent (Heinz et al, 2005; Weerts et al, 2011; Williams et al, 2009). It is not possible, however, to determine from our data if there are changes in MOR availability as abstinence progresses, or if our participants had ‘normal’ MOR availability early in their recovery and this was a contributory factor to their ability to achieve a longer and more stable abstinence. Also, we do not have follow-up relapse data relating in our participants and therefore it is not possible to examine what impact MOR availability had on relapse risk in our cohort.

265 There were no associations between any clinical measures and MOR availability in our abstinent alcohol dependent participants. However associations between high MOR availability and higher craving scores in alcohol and cocaine dependence are commonly described in the published literature (Gorelick et al, 2005; Heinz et al, 2005; Williams et al, 2009; Zubieta et al, 1996). Our alcohol dependent participants did not report any alcohol craving, likely due to their stable recovery and longer durations of abstinence. The lack of reported craving may also be related to the finding that our alcohol dependent participants did not have high MOR availability.

Finally, we found lower MOR availability in individuals carrying the OPRM1 G-allele polymorphism and this has been reported in a number of other studies (Domino et al, 2015; Nuechterlein et al, 2016; Peciña et al, 2015b; Ray et al, 2011; Weerts et al, 2013). There was no differential effect of the OPRM1 G-allele on MOR availability between alcohol dependent participants and healthy controls.

6.2. Oral dexamphetamine-induced endogenous opioid release is blunted in alcohol dependent participants

It was hypothesised that similarly to findings in gambling disorder (Mick et al, 2016), there would be blunted oral dexamphetamine-induced endogenous opioid release in abstinent alcohol dependent participants. This blunted endogenous opioid release was shown across almost all high MOR binding ROIs investigated (17 out of 21 ROIs) in alcohol dependent participants, indicating a ‘global’ blunting of endogenous opioid tone. There was no evidence of an association between dexamphetamine-induced endogenous opioid release and duration of abstinence, suggesting that blunted endogenous opioid release persists in abstinence and does not appear to recover. It is also possible that low endogenous opioid tone is present prior to developing addiction and may represent a pre-existing risk factor.

A comparison of blunted oral dexamphetamine-induced endogenous opioid release between abstinent alcohol dependent and gambling disorder participants did not show any significant

266 differences in the magnitude of this blunted endogenous opioid tone. This suggests that low endogenous opioid tone may be a common factor to both behavioural and substance additions. The similarity of our results in gambling disorder and alcohol dependence also suggests that previous chronic heavy alcohol may not have an additional impact on blunting endogenous opioid tone in longer term abstinent alcohol dependent individuals. The blunted oral dexamphetamine-induced endogenous opioid release in alcohol dependence and gambling disorder may be related to a lack of salience of dexamphetamine as reward in these addictions.

Similar to the MOR availability results, there were no significant associations between endogenous opioid release and clinical variables associated with alcohol dependence. The lack of an association between oral dexamphetamine-induced endogenous opioid release and duration of abstinence suggests that low endogenous opioid tone endures into abstinence and may reflect a continued vulnerability for relapse to alcohol use. Unfortunately, we do not have follow-up data to investigate if lower endogenous opioid tone is associated with a higher risk of relapse.

6.3. Associations between MID financial reward anticipation responses, MOR availability and endogenous opioid release

By combining fMRI MID task and [11C]carfentanil PET data it was shown that in alcohol dependence higher putamen MOR availability was associated with lower MID win>neutral anticipation BOLD contrast. However, it not clear if this link is due to opioidergic modulation of the MID win anticipation BOLD responses or due to another variable that is associated with, or mediating, both MID task BOLD and [11C]carfentanil PET measures in alcohol dependence.

It is possible that the link between MOR availability and MID win>neutral anticipation BOLD contrast may be associated with clinical factors such as impulsivity or relapse risk, but it was not possible to explore this further in our current dataset. The associations between MOR availability and MID win>neutral anticipation BOLD contrast may reflect associations between

267 MOR availability and dopaminergic tone in alcohol dependence, but further research is required to explore this hypothesis.

Oral dexamphetamine-induced endogenous opioid release and MID win>neutral anticipation BOLD contrast were negatively correlated in gambling disorder. This may reflect an association between lower opioidergic tone and higher dopaminergic tone in gambling disorder, although further research is required to examine the associations between dopaminergic and opioidergic signalling in addiction and understand the clinical implications of these associations.

Another possibility is that the associations between MID win>neutral anticipation BOLD contrast and dexamphetamine-induced endogenous opioid release are due to differences in the relative salience of financial rewards and a dexamphetamine challenge in gambling disorder. For example, an individual with more blunted responses non-salient stimuli or rewards will have higher responses to salient rewards or stimuli. This divergence in responses to salient versus non-salient stimuli in addiction may indicate a greater severity of dependence and a higher risk of relapse. Unfortunately, we do not have follow-up relapse data in either alcohol dependent or gambling disorder participants to examine this.

As noted in Section 5.4.4., our MID win>neutral anticipation BOLD contrast and dexamphetamine-induced endogenous opioid release measures represent two different methods of stimulation (i.e. a pharmacological challenge versus a behavioural reward task) with different outcome metrics that occurred typically two to four weeks apart. The use of simultaneous PET/fMRI imaging would allow endogenous opioid release during the MID task to be examined, and this could be used to understand if there is activation of opioidergic signalling during the MID task, and how this relates to reward anticipation BOLD responses.

Finally, in healthy controls there was evidence that OPRM1 A188G polymorphism G-allele carriers had higher MID win>neutral anticipation BOLD contrast. Including OPRM1 A188G polymorphism in our analyses as a covariate did not change our significant results in alcohol dependence and gambling disorder. However, given the findings an effect of the OPRM1 genotype on both MOR availability and MID task BOLD responses suggests that the possible

268 interaction effects of the OPRM1 genotype need to be better understood for further work combining MOR PET and fMRI measures.

6.4. Comparisons between alcohol dependence and gambling disorder

As stated in Section 1.2. there are a number of advantages to comparing alcohol dependent participants with gambling disorder participants. Firstly, and potentially most importantly, as gambling disorder is a behavioural addiction there is no repeated dosing of a pharmacological agent which may have long term effects on neurotransmission. This is in contrast with alcohol dependence where there is chronic consumption of high doses of alcohol, which has effects on a wide range of neurotransmission systems including GABA (Lobo and Harris, 2008), dopamine (Ramchandani et al, 2011) and endogenous opioids (Mitchell et al, 2012). Therefore, it can be difficult to ascertain if changes in PET measures of neuroreceptor availability or endogenous neurotransmitter release in alcohol dependence are due to the pharmacological effects of chronic alcohol consumption, or a result of the behavioural process of addiction.

Behavioural addictions, such as gambling disorder, share a number of characteristics with substance dependence (Grant et al, 2016), and therefore, finding commonalities in the dysregulation of the endogenous opioid system between alcohol dependence and gambling disorder may suggest that this dysregulation is a common underlying factor in both substance and behavioural addictions, rather than just a consequence of chronic heavy alcohol use (see Section 4.4.4.). There is evidence that opioid receptor antagonists (e.g. naltrexone and nalmefene) are effective in relapse prevention in gambling disorder (Grant et al, 2014) as well as alcohol dependence (Lingford-Hughes et al, 2012). Our evidence of shared endogenous opioid signalling dysregulation in both alcohol dependence and gambling disorder may indicate a shared mechanistic target for opioid receptor antagonist treatment in both of these addictions. This could suggest that future interventions targeting endogenous opioid signalling found to be effective in one of these addictions may also be an effective treatment in the other.

269 Furthermore, as discussed in Section 5.1.2., including the gambling disorder participants in the combined fMRI and PET analysis allowed the potential effects salience on endogenous opioid release and MID task win>neutral anticipation BOLD contrast to be explored. Although unfortunately the interpretation of the results from these analyses is less clear than the comparison off endogenous opioid release Chapter 4.

There are limitations to our comparisons between alcohol dependent and gambling disorder participants, including differences in abstinence and current psychopathology, which are discussed in more detail in Section 4.4.14. and Section 5.4.9. . However, it is still of interest to compare these two addictions and the dataset used in this thesis provides a unique opportunity to examine dysregulated endogenous opioid signalling, MID task win>neutral anticipation BOLD contrast and combined PET/fMRI measures in both substance and behavioural addictions.

6.5. Abstinence and relapse risk

In Chapter 3 and Chapter 4 we did not find any significant associations between abstinence duration and either MOR availability or dexamphetamine induced endogenous opioid release. It is unclear if this is due to using a sample of alcohol dependent participants who have long durations of abstinence or due to other factors.

It must be acknowledged that there are some limitations to our measures of abstinence duration. Our duration of abstinence was based on each participants’ self-reported duration of time free from alcohol. We also required participants to provide an alcohol-free breathalyser reading. However, the human body metabolises approximately 7 grams (~1 UK unit) of alcohol per hour (Swift, 2003), therefore alcohol breathalysers would be very unlikely to detect any alcohol consumption greater than 24 hours prior to the study visit.

Studies examining MOR availability in alcohol dependence during early abstinence typically required an inpatient admission for detoxification prior to the scan and participants remained

270 as inpatients with repeated random alcohol breathalyser testing until their [11C]carfentanil PET scans (Heinz et al, 2005; Weerts et al, 2011) ensuring abstinence from alcohol. This is, however, impractical for participants with longer periods of abstinence. Some clinical trials examining abstinence from alcohol over longer periods of time required participants to attend for regular visits (i.e. every 1-2 weeks) where they were breathalysed for alcohol and provided a recent alcohol use history (Addolorato et al, 2007; Johnson et al, 2003). Family members could also be asked to attend to provide a collateral alcohol use history as well (Addolorato et al, 2007). This requires prospective recruitment of participants with a possibly lengthy period of follow-up appointments, both of which would not have been possible in our [11C]carfentanil PET studies for time and logistical reasons. Furthermore, this method does not resolve the relatively short time period following alcohol consumption required for breathalyser testing to detect alcohol use. One potential method that could be used to confirm alcohol use over a longer period of time is hair testing for alcohol metabolite ethyl glucuronide (Politi et al, 2006; Pragst and Balikova, 2006).

Another limitation of our study is that we have no relapse data for our participants, and therefore the interpretation of our findings in respect to relapse risk is limited. One method for examining relapse risk would be to follow our participants up after the [11C]carfentanil PET scans to record whether they relapse, and the duration of time until relapse. Similar follow- up methods could be used as discussed above with regular follow-up appointments to collect data on alcohol use and carry out breathalyser readings. If this was not practical then telephone follow-up contact could be conducted to collect alcohol use and relapse data, and this which was the follow-up method used in the ICCAM study (Paterson et al, 2015). With either of these follow-up methods there is the issue of participants being lost to follow-up and it is likely that a larger number of participants would have to be recruited and scanned to provide a suitable number of participants with follow-up relapse data. Furthermore, in participants with long durations of abstinence and potentially lower risk of relapse a long- term follow-up, i.e. 6-12 months or longer, may be required to obtain usable relapse data. Another issue is how to interpret alcohol use data, as there is no single definition of what constitutes a significant relapse (Lingford-Hughes et al, 2012). In some studies, a relapse is defined as any alcohol use (Rösner et al, 2010). In other studies relapse is recorded as alcohol consumption above a certain threshold, for example a commonly used definition of ‘heavy

271 drinking’ as 5 or more drinks (1 drink is 14 grams of alcohol) in a single day for men or 4 or more drinks in a single day for women (Anton, 1996; Oslin et al, 2015).

It is also possible to predict future relapse risk using questionnaires or clinical data collected at the time of the study. We used the time to relapse questionnaire (TRQ) (Adinoff et al, 2010) in our study, although there are a range of other measures that have been shown to predict relapse in alcohol dependence. For example higher trait anxiety (Willinger et al, 2002), depressive symptoms (Abulseoud et al, 2013), stress (Sinha, 2012) and craving (Heinz et al, 2009) have all been shown to be associated with future relapse risk. There are fMRI measures that have also been shown to be associated with relapse including alcohol cue reactivity responses (Courtney et al, 2016) and neural activity during a working memory task (Charlet et al, 2014), as have measures of cortical thickness (Durazzo et al, 2011). The wide range of factors suggests a degree of complexity when predicting relapse and a multifactorial approach would most likely be required to prospectively predict which individuals are at higher risk of relapse.

6.6. Potential clinical implications of these results

6.6.1. Opioid receptor agonist treatment in alcohol dependence

As discussion in the introduction of this thesis (see Section 1.1.) there is good evidence for the use of opioid receptor antagonist treatment (e.g. naltrexone and nalmefene) in alcohol dependence. However, there is heterogeneity in the treatment responses to opioid receptor antagonists (Garbutt et al, 2014) and naltrexone only reduces the risk of returning to heavy drinking within 3 to 12 months of abstinence by 14% compared with placebo (Rösner et al, 2010). This highlights an unmet need in understanding which alcohol dependent individuals are most likely to respond to opioid receptor antagonist treatment.

One hypothesis for the mechanism of opioid receptor antagonists reducing risk of relapse in alcohol dependence is targeting the high MOR availability in early abstinence, possibly by

272 lowering alcohol craving associated with this high MOR availability (Heinz et al, 2005). It has been shown that individuals with higher MOR availability are more likely to benefit from naltrexone treatment in early abstinence, although there is also an interaction with the OPRM1 A118G polymorphism (Hermann et al, 2017). However, our alcohol dependent participants do not have higher MOR availability and were stable in their abstinence with no reported alcohol craving. Therefore, the mechanistic target for opioid receptor antagonist treatment of high MOR availability associated craving may not be relevant in this group of longer-term abstinent alcohol dependent participants.

Our combined MID fMRI and MOR availability analysis shows that individuals with higher MOR availability have a more blunted MID win>neutral anticipation BOLD contrast, which, based on data from the ICCAM cohort (Paterson et al, 2017), may reflect a higher risk of relapse. It is not clear if this association between low MID win>neutral anticipation BOLD contrast and relapse is due to higher MOR availability, which may be a target for opioid receptor antagonists, or whether it reflects a dopaminergic signalling dysfunction that is leading to a higher risk of relapse. The published ICCAM MID results, in a cohort of alcohol dependent participants with similar recovery characteristics to our cohort, may suggest that modulating dopaminergic function with D3 receptor antagonist GSK598809 may be more beneficial in these participants than naltrexone, which does not appear to have an impact on MID BOLD responses in alcohol dependence (Murphy et al, 2017; Nestor et al, 2017).

Opioid receptor antagonists block the agonist effects of both endogenous and exogenous opioid ligands. Given the low endogenous opioid tone shown in alcohol dependence, it might be questioned what effect opioid receptor antagonists might have if there are no endogenous opioid agonist ligands to block. However, naltrexone may not be most beneficial in increasing abstinence durations (i.e. counting any alcohol consumption as an end to abstinence) but rather in reducing the risk of a drinking ‘lapse’ becoming a relapse (i.e. relapse to heavy dependent drinking) (Lingford-Hughes et al, 2012; Rösner et al, 2010).

Dexamphetamine-induced endogenous opioid release is blunted in alcohol dependence, but alcohol-induced endogenous opioid release may not be, and could possibly be enhanced (Chapter 4, Section 4.4.3.). Therefore a ‘lapse’ in abstinence (i.e. alcohol consumption) may

273 induce substantial endogenous opioid release and reinforce further alcohol consumption. By blocking the reinforcing effect of this alcohol-induced endogenous opioid release, naltrexone may halt the progression of a lapse to a relapse. Nalmefene has been shown to reduce MID win>neutral anticipation BOLD contrast in non-treatment seeking alcohol dependent individuals receiving an intravenous alcohol infusion (Quelch et al, 2017), suggesting a blunting of the effects of alcohol-induced endogenous opioid release to reinforce the MID financial rewards following opioid receptor antagonist treatment.

There is also evidence of the role of opioid receptor antagonists in modulating brain responses to alcohol cues (Schacht et al, 2013a, 2013b), possibly related to cue-induced activation of endogenous opioid signalling. The effectiveness of naltrexone treatment in reducing heavy drinking is correlated with the degree to which it blunts these responses to alcohol cues (Schacht et al, 2017). It is possible that reducing the risk of cue related relapse is another potential target for opioid receptor antagonist treatment in abstinent alcohol dependent participants.

Opioid receptor antagonists may be most effective when targeted to abstinent individuals who are at higher risk of lapses. This includes during early abstinence, and in individuals with higher levels of dependence (ADS scores) and poorer resources for coping (e.g. negative thinking, avoidance, lack of social support) (Witkiewitz and Masyn, 2008). Furthermore, genotyping individuals for the OPRM1 A118G polymorphism may further improve targeted treatment given the evidence of better naltrexone treatment responses in G-allele carriers (Hermann et al, 2017).

6.6.2. Low endogenous opioid tone in alcohol dependence

If low endogenous opioid tone is a risk factor for relapse in alcohol dependence, then boosting endogenous opioid signalling may be a beneficial treatment. Opioid agonists are an effective way of ‘boosting’ opioidergic signalling but these drugs have dangerous side effects (Florence et al, 2016), particularly respiratory depression which is further exacerbated when used in conjunction with alcohol (van der Schrier et al, 2017). Furthermore, opioid agonists are highly

274 addictive and therefore are unlikely to be suitable for long-term use in individuals with alcohol dependence.

Pharmacological agents that can increase opioiderigc signalling without the harmful side- effects and abuse potential of opioid drugs may be of potential interest in treating alcohol dependence. The negative effects of MOR agonists (i.e. respiratory depression, constipation and tolerance) are primarily mediated through the activation of a β-arrestin regulatory pathway (Bohn et al, 2000; Raehal et al, 2005). ‘Biased’ MOR agonists do not activate this β- arrestin pathway and there are a number of these compounds (e.g. oliceridine) currently in development as novel opioid analgesic agents (Singla et al, 2017) with potentially less risk of respiratory depression or developing opioid dependence. However the abuse potential of biased MOR agonists in humans needs to be fully investigated before they could be considered as a treatment in addiction, particularly as some animal studies have shown evidence of abuse potential (Altarifi et al, 2017).

6.7. Further research

6.7.1. Exploring endogenous opioid tone with [11C]carfentanil PET in individuals at high risk of developing alcohol dependence

To establish if low endogenous opioid tone is a risk factor for, or consequence of, alcohol dependence, a similar [11C]carfentanil PET and oral dexamphetamine challenge study could be carried out in individuals with a higher risk of alcohol dependence. Individuals with a first degree family history of alcohol dependence have been shown to have low plasma β- endorphin concentrations (Dai et al, 2005), therefore may be an interesting high risk group to investigate oral dexamphetamine-induced endogenous opioid release in. However, it is possible that these individuals have some resilience factor against alcohol dependence which is why they haven’t become alcohol dependent, and this could confound findings if this resilience factor relates to endogenous opioid signalling.

275 A cohort study of a larger population of young adults may be more useful to establish if low opioid function predicts later alcohol dependence. However, low opioid function may not be a stable trait and may develop a later point in adulthood prior to alcohol dependence, and therefore longitudinal scanning of the sample would also be useful. However, PET scanning is expensive and has risks of developing cancer associated with the radiation exposure (Huang et al, 2009). Large sample PET studies may, therefore, be prohibitively expensive and are likely to carry an unacceptable health risk, particularly in younger adults.

6.7.2. Exploring blunted endogenous opioid release in other addictions

Given the evidence of blunted oral dexamphetamine-induced opioid release in both alcohol dependence and gambling disorder (Mick et al, 2016), it would be of interest to examine its presence in other addictions to understand if low endogenous opioid tone is a factor common to all addictions. However, dexamphetamine challenge may not be suitable in abstinent stimulant addicts due to the ethical concerns of giving amphetamine to abstinent stimulant addicts. However, if it is expected that the non-salience of dexamphetamine explains blunted endogenous opioid release then a study in actively using stimulant dependent individuals, where dexamphetamine may be salient, could be expected to show enhanced endogenous opioid release following dexamphetamine challenge.

6.7.3. Exploring the relationship between low endogenous opioid tone and risk of relapse

Unfortunately, our data did not include any follow-up data to monitor for risk of relapse and examine whether there is an association of higher relapse with lower endogenous opioid tone. This would be of interest given the possibility of low opioid tone being a risk factor for relapse and therefore a possible target for relapse prevention. Studies examining associations between MOR availability and relapse in cocaine dependence had sample sizes of 17 to 25 cocaine dependent participants (Ghitza et al, 2010; Gorelick et al, 2005, 2008). It is possible that similar numbers or more would be required to examine the association between

276 dexamphetamine-induced endogenous opioid release and relapse in alcohol dependence. Again, the high cost of PET imaging may be a constraining factor, particularly if there would be a high dropout from follow-up.

6.7.4. Examining associations between oral dexamphetamine challenge-induced

11 reductions in [ C]carfentanil BPND and changes in plasma β-endorphin concentrations

In the gambling disorder and alcohol dependence studies included in this thesis, plasma samples were collected to measure changes in peripheral β-endorphin concentrations following the oral dexamphetamine challenge. Unfortunately, it was not possible to analyse these samples due to difficulties in finding a suitable working assay. Finding blunted plasma β-endorphin responses to the oral dexamphetamine challenge in alcohol dependent participants would provide validation of our findings of blunted central nervous system (CNS) endogenous opioid release. Plasma β-endorphin levels may also help to understand if there is a delay in dexamphetamine-induced endogenous opioid release as suggested by the lack of

11 detectable changes in [ C]carfentanil BPND immediately following intravenous dexamphetamine administration (Guterstam et al, 2013).

Finally, plasma β-endorphin levels could be used instead of [11C]carfentanil PET imaging to assess the relationships between low opioid tone and risk of developing alcohol dependence or risk of relapse in abstinent alcohol dependent participants. This would eliminate the radiation exposure risks from these studies and may be relatively inexpensive compared to PET imaging.

Peripheral and CNS β-endorphin may not be directly comparable as they are produced by cleavage of pro-opiomelanocortin (POMC) in separate sites: pituitary for peripheral and arcuate nucleus of hypothalamus for central. However the release of both can be stimulated in the same way with intense exercise, dexamphetamine and alcohol (Cohen et al, 1981; Colasanti et al, 2012; Dai et al, 2005; Goldfarb and Jamurtas, 1997; Mick et al, 2014; Mitchell et al, 2012; Saanijoki et al, 2018) and a comparison between reductions in [11C]carfentanil

277 BPND and changes in plasma β-endorphin concentrations following oral dexamphetamine challenge may inform if changes in peripheral β-endorphin concentrations are a valid indicator of CNS endogenous opioid release.

6.7.5. Examining the association between salience and blunted endogenous opioid release

The non-salience of dexamphetamine may be a factor in the blunted endogenous opioid response in alcohol dependence. Whilst it may be hypothesised that in alcohol dependence a salient alcohol challenge would be associated with higher endogenous opioid release, this cannot be tested in abstinent alcohol dependent participants. This may be possible in dependent and non-treatment seeking drinkers, as was done in the Quelch et al. (2017) study using an intravenous alcohol challenge and nalmefene. However, recent alcohol use may confound measurements of endogenous opioid release due to the effect of alcohol on opioidergic signalling (Mitchell et al, 2012). Another possibility is to use behavioural paradigms which have been shown to induce endogenous opioid release, for example social touch or social laughter tasks (Manninen et al, 2017; Nummenmaa et al, 2016). Alcohol cues may be a suitable behavioural task to induce endogenous opioids release as blunting of fMRI BOLD responses to alcohol cues with naltrexone suggests there may be some degree of activation of endogenous opioid signalling by alcohol cues in alcohol dependence (Schacht et al, 2013a).

In gambling disorder, there would be less of an issue of pharmacological dysregulation of endogenous opioid signalling in current gamblers who are not using psychoactive drugs. In this case gambling tasks, such as slot machines or fixed odds betting terminals could be used to explore the effects of addiction related ‘salient’ rewards on endogenous opioid release.

The timing of these behavioural tasks in relation to the scans would have to be carefully considered: for example, whether a task administered during the scan would be adequate to produce a measurable reduction in [11C]carfentanil or would a period of time be required

278 prior to the scan to allow a measurement of reduced [11C]carfentanil binding such as the three hours required with dexamphetamine (Colasanti et al, 2012).

6.7.6. Exploring blunted endogenous opioid release in female alcohol dependent individuals

Recruitment in our studies examining endogenous opioid release in healthy controls, alcohol dependent and gambling disorder participants was limited to males. Female participants were not recruited partly due to evidence of associations between higher plasma correlations

11 of estradiol during the follicular phase of the menstrual cycle and lower [ C]carfentanil BPND (Smith et al, 1998). This raised a concern that imaging women in different stages of the menstrual cycle may have a confounding effect on our measures of MOR availability and endogenous opioid release. However, a study using [11C]carfentanil PET and oral dexamphetamine challenge in female alcohol dependent participants would be required to examine if our findings of blunted endogenous opioid release are limited to male alcohol dependent individuals or not. One possible method for controlling for the potential effect of

11 11 menstrual cycle on [ C]carfentanil BPND would be to carry out [ C]carfentanil PET scanning in all female participants at similar stages in the menstrual cycle.

6.7.7. Exploring endogenous opioid release during the MID task and the effect of opioid signalling on MID task win>neutral anticipation BOLD contrast

Opioid receptor antagonists do not modulate MID win>neutral anticipation BOLD contrast in healthy controls or alcohol dependent participants (Nestor et al, 2017), which may indicate a lack of endogenous opioid signalling during this task. Further research is required to confirm this, and a modified MID task similar to the one used by Schott et al. (2008) to examine dopamine release could be used to examine endogenous opioid release. Simultaneous [11C]carfentanil PET and MID task fMRI may also allow correlations to be examined between win>neutral anticipation BOLD contrast and endogenous opioid release.

279 It is hypothesised that nalmefene blunts MID win>neutral anticipation BOLD contrast during an intravenous alcohol infusion by blocking the effect of alcohol-induced endogenous opioid release to enhance mesolimbic dopaminergic signalling (Quelch et al, 2017). If there is endogenous opioid release in gambling disorder participants during the MID task due to the salience of the financial reward, then it might also be expected that opioid receptor antagonists (e.g. naltrexone or nalmefene) may lower the win>neutral anticipation BOLD contrast.

Finally, there is no published literature examining the effects of opioid agonists on MID win>neutral anticipation BOLD contrast. Research examining this would provide an insight as to whether opioid receptor antagonists do enhance task related mesocorticolimbic dopaminergic signalling or not.

6.8. Conclusions

A dysregulation of endogenous opioid signalling has been proposed to play an important role in addiction with previous findings of higher MOR availability in alcohol and cocaine dependence during early abstinence. We did not find evidence of higher MOR availability in long-term abstinent alcohol dependent participants. However, we did show for the first time that abstinent alcohol dependent individuals have blunted dexamphetamine-induced endogenous opioid release. This is similar to previous findings in gambling disorder and suggests that low endogenous opioid tone, rather than high MOR availability, may be a common feature in both substance and behavioural addictions. Low endogenous opioid tone may also be an important factor in the risk of developing addiction or in relapse following treatment.

We have also shown for the first time that MOR availability and endogenous opioid tone are associated with MID task reward anticipation BOLD responses in alcohol dependence and gambling disorder. This may reflect the associations between dysregulated endogenous opioid signalling and reward processing in addiction, or a relationship between MORs,

280 endogenous opioid signalling and dopaminergic signalling. Further research is required to better understand the factors that may be mediating these associations, and the implications of these findings on the use of opioid receptor antagonists in the treatment of addiction.

281

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316 8. APPENDIX

8.1. Peer reviewed Journal publications

2018 Turton S, Myers JF, Mick I, et al. “Blunted endogenous opioid release following an oral dexamphetamine challenge in abstinent alcohol-dependent individuals.” Molecular Psychiatry, (Epub ahead of print 25th June 2018).

2018 Durant CF, Paterson LM, Turton S, et al. “Using Baclofen to Explore GABA-B Receptor Function in Alcohol Dependence: Insights From Pharmacokinetic and Pharmacodynamic Measures.” Front Psychiatry. 9:664

2018 Venkataraman AV, Keat N, Myers JF, Turton S, et al. “First evaluation of PET-based human biodistribution and radiation dosimetry of 11C-BU99008, a tracer for imaging the imidazoline2 binding site.” EJNMMI Res. 8:71

2018 Tyacke RJ, Myers JFM, Venkataraman A, Mick I, Turton S, et al. “Evaluation of 11C- BU99008, a positron emission tomography ligand for the Imidazoline2 binding site in human brain.” J Nucl Med 59:1597-1602

2016 Nahar LK, Cordero RE, Nutt D, … Turton S, et al. “Validated Method for the Quantification of Baclofen in Human Plasma Using Solid-Phase Extraction and Liquid Chromatography-Tandem Mass Spectrometry.” J Anal Toxicol. 40: 117-123

8.2. Non-peer reviewed Journal publications

2016 Turton S, Lingford-Hughes A “Neurobiology and principles of addiction and tolerance.” Medicine (UK). 44:693-696.

8.3. Book chapters

(Accepted and awaiting publication) Venkataraman AV, Turton S, Lingford-Hughes A. “Drugs and Toxins as Causes of Neuropsychiatric Conditions” in Oxford Textbook of Neuropsychiatry - Oxford University Press

8.4. Conference Abstracts

2018 Turton S, Myers JFM, et al. “Combining mu-opioid receptor availability and endogenous opioid release with functional MRI measures of reward anticipation in alcohol and gambling addiction”.

317 Oral Presentation: NRM 2018 Mapping NeuroReceptors at Work, London.

2017 Turton S, Mick I, et al. “Individuals with gambling disorder and alcohol dependence have blunted endogenous opioid release compared with healthy volunteers, measured with [11C]carfentanil PET and oral dexamphetamine challenge” Poster Presentation: British Association of Psychopharmacology Summer Meeting, Harrogate.

2017 Turton S, Myers JFM, et al. “Comparing m-opioid receptor availability and opioid/β- endorphin release between individuals with gambling disorder, alcohol dependence and healthy volunteers using [11C]carfentanil PET and dexamphetamine challenge”. Oral Presentation: 28th Symposium on Cerebral Blood Flow, Metabolism and Function, Berlin.

2016 Turton S, Durant C, et al. “A pharmacokinetic and dynamic study of the GABA-B receptor system in alcohol dependence”. Poster Presentation: ISBRA ESBRA World Congress on Alcohol an Alcoholism, Berlin.

2016 Turton S, Myers JFM et al. “Blunted endogenous opioid release in abstinent alcohol dependent patients examined using [11C]carfentanil PET and dexamphetamine” Poster Presentation: NRM 11th International Symposium on Functional NeuroReceptor Mapping of the Living Brain, Boston.

2016 Turton S, Myers JFM, et al. “Alcohol dependent patients have blunted endogenous opioid release measured using [11C]carfentanil PET and dexamphetamine challenge” Poster Presentation: 29th European College of Neuropsychopharmacology (ECNP) Congress, Vienna.

8.5. Other presentations

2017 “Investigating the μ-opioid receptor and endogenous opioid release in Alcohol Dependence using [11C]carfentanil PET and dexamphetamine challenge” Royal College of Psychiatry Faculty of Addictions Psychiatry Annual Scientific Conference. London.

8.6. Awards and Prizes

2017 28th Symposium on Cerebral Blood Flow, Metabolism and Function Berlin 2017: Early Career Investigator Travel Award (€650).

2015 European College of Neuropsychopharmacology (ECNP) travel bursary to attend ECNP Clinical Research Methods Workshop, Barcelona 2015 (€150).

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