Alcohol use disorder (AUD) is a widespread mental disease denoted by chronic alcohol use despite significant negative consequences for a person’s life. It affected more than 14 million persons in Europe alone and accounted for more than 5% of deaths worldwide in 2011-2012. Understanding the psychological and neurobiological mechanisms driving the development and maintenance of pathological alcohol use is key to conceptualizing new programs for prevention and therapy of AUD. There has been a variety of etiological models trying to describe and relate these mechanisms. Lately, the view of AUD as a disorder of learning and decision making has received much support proposing dual systems to be at work in AUD – one system being deliberate, forward-planning, and goal-directed and the other one reflexive, automatic, and habitual. Both systems supposedly work in parallel in a framework of value-based decision making and their balance can be flexibly adjusted in healthy agents, while a progressive imbalance favoring habitual over goal-directed choice strategies is assumed in AUD. This imbalance has been theoretically associated to neural adaptations to chronic alcohol use in corticostriatal pathways involved in reward processing, especially in ventral striatum. However, these theoretical models are grounded strongly on animal research while empirical research in the human domain remains rather sparse and inconclusive. Furthermore, alterations in value-based decision-making processes and their neural implementation might not only result from prolonged alcohol misuse but may also represent premorbid interindividual differences posing a risk factor for the development of AUD. Therefore, I here present three studies investigating the relation of alcohol use with the balance between goal-directed and habitual decision systems and with parameters modulating option valuation processes of these systems, namely delay, risk, and valence of option outcomes. To separate the investigation of these decision processes as predisposing risk for or consequence of alcohol use, two samples were examined: one sample of 201 eighteen-year-old men being neither abstinent from nor dependent on alcohol as well as one sample of 114 AUD patients in detoxification treatment and 98 control participants matched for age, sex, educational background, and smoking status. Both samples had a baseline assessment of several behavioral tasks, questionnaires, and neuropsychological testing and were followed-up over one year to examine drinking trajectories in the sample of young men and relapse in detoxified patients. The behavioral tasks included a sequential choice task using model-free and model-based reinforcement learning as operationalization of habitual and goal-directed decision making, respectively, during functional magnetic resonance imaging and four tasks probing participants’ delay discounting, probability discounting for gains and losses, and loss aversion. Study 1 presents the cross-sectional analysis of the sequential choice task in relation to baseline drinking behavior of the young-adult sample. These analyses did not reveal an association between non-pathological alcohol use and habitual and goal-directed control on neither a behavioral nor neural level except for one exploratory finding of increased BOLD responses to model-free habitual learning signals in participants with earlier onset of drinking. Study 2 examined the same task in AUD patients compared to control participants showing no difference in behavioral control or neural correlates between those groups. However, prospectively relapsing AUD patients showed lower BOLD responses associated to model-based goal-directed control than abstaining patients and control participants. Additionally, the interaction of goal-directed control and positive expectancies of alcohol effects discriminated subsequently relapsing and abstaining patients revealing an increased risk of relapse for those patients who showed higher levels of goal-directed control and low alcohol expectancies or low levels of goal-directedness and high expectancies. Study 3 examined modulating features of goal-directed and habitual option valuation – delay, risk, and valence of options – in association to alcohol use in the young-adult sample and AUD status in the sample of patients and matched control participants on a cross-sectional as well as longitudinal level. This study revealed no relation of delay, risk, and loss aversion with current alcohol use and consumption one year later in the young men. In contrast, AUD patients showed systematically more impulsive choice behavior than control participants in all four tasks: a higher preference for immediate rewards, more risky choices when facing gains and less when facing losses, and lower loss aversion. Furthermore, a general tendency to overestimate the probability of uncertain losses could predict relapse risk over the following year in AUD patients. Taken together, these results do not support the hypothesis that mechanisms of value-based decision making might be predisposing risk factors for alcohol consumption. The findings for patients already suffering from AUD are mixed: while choice biases regarding delays, risks, and valence of option outcomes seem to be altered systematically in AUD, there was no indication of an imbalance of habitual and goal-directed control. These findings challenge the assumption of a generalized outcome-unspecific shift of behavioral control from goal-directed to habitual strategies during the development of AUD and point towards several possible future avenues of research to modify or extend the theoretical model.

Alcohol use disorder (AUD) is a widespread mental disease denoted by chronic alcohol use despite significant negative consequences for a person’s life. It affected more than 14 million persons in Europe alone and accounted for more than 5% of deaths worldwide in 2011-2012. Understanding the psychological and neurobiological mechanisms driving the development and maintenance of pathological alcohol use is key to conceptualizing new programs for prevention and therapy of AUD. There has been a variety of etiological models trying to describe and relate these mechanisms. Lately, the view of AUD as a disorder of learning and decision making has received much support proposing dual systems to be at work in AUD – one system being deliberate, forward-planning, and goal-directed and the other one reflexive, automatic, and habitual. Both systems supposedly work in parallel in a framework of value-based decision making and their balance can be flexibly adjusted in healthy agents, while a progressive imbalance favoring habitual over goal-directed choice strategies is assumed in AUD. This imbalance has been theoretically associated to neural adaptations to chronic alcohol use in corticostriatal pathways involved in reward processing, especially in ventral striatum. However, these theoretical models are grounded strongly on animal research while empirical research in the human domain remains rather sparse and inconclusive. Furthermore, alterations in value-based decision-making processes and their neural implementation might not only result from prolonged alcohol misuse but may also represent premorbid interindividual differences posing a risk factor for the development of AUD. Therefore, I here present three studies investigating the relation of alcohol use with the balance between goal-directed and habitual decision systems and with parameters modulating option valuation processes of these systems, namely delay, risk, and valence of option outcomes. To separate the investigation of these decision processes as predisposing risk for or consequence of alcohol use, two samples were examined: one sample of 201 eighteen-year-old men being neither abstinent from nor dependent on alcohol as well as one sample of 114 AUD patients in detoxification treatment and 98 control participants matched for age, sex, educational background, and smoking status. Both samples had a baseline assessment of several behavioral tasks, questionnaires, and neuropsychological testing and were followed-up over one year to examine drinking trajectories in the sample of young men and relapse in detoxified patients. The behavioral tasks included a sequential choice task using model-free and model-based reinforcement learning as operationalization of habitual and goal-directed decision making, respectively, during functional magnetic resonance imaging and four tasks probing participants’ delay discounting, probability discounting for gains and losses, and loss aversion. Study 1 presents the cross-sectional analysis of the sequential choice task in relation to baseline drinking behavior of the young-adult sample. These analyses did not reveal an association between non-pathological alcohol use and habitual and goal-directed control on neither a behavioral nor neural level except for one exploratory finding of increased BOLD responses to model-free habitual learning signals in participants with earlier onset of drinking. Study 2 examined the same task in AUD patients compared to control participants showing no difference in behavioral control or neural correlates between those groups. However, prospectively relapsing AUD patients showed lower BOLD responses associated to model-based goal-directed control than abstaining patients and control participants. Additionally, the interaction of goal-directed control and positive expectancies of alcohol effects discriminated subsequently relapsing and abstaining patients revealing an increased risk of relapse for those patients who showed higher levels of goal-directed control and low alcohol expectancies or low levels of goal-directedness and high expectancies. Study 3 examined modulating features of goal-directed and habitual option valuation – delay, risk, and valence of options – in association to alcohol use in the young-adult sample and AUD status in the sample of patients and matched control participants on a cross-sectional as well as longitudinal level. This study revealed no relation of delay, risk, and loss aversion with current alcohol use and consumption one year later in the young men. In contrast, AUD patients showed systematically more impulsive choice behavior than control participants in all four tasks: a higher preference for immediate rewards, more risky choices when facing gains and less when facing losses, and lower loss aversion. Furthermore, a general tendency to overestimate the probability of uncertain losses could predict relapse risk over the following year in AUD patients. Taken together, these results do not support the hypothesis that mechanisms of value-based decision making might be predisposing risk factors for alcohol consumption. The findings for patients already suffering from AUD are mixed: while choice biases regarding delays, risks, and valence of option outcomes seem to be altered systematically in AUD, there was no indication of an imbalance of habitual and goal-directed control. These findings challenge the assumption of a generalized outcome-unspecific shift of behavioral control from goal-directed to habitual strategies during the development of AUD and point towards several possible future avenues of research to modify or extend the theoretical model.:Table of Contents List of Figures List of Tables List of Abbreviations Abstract Chapter 1. Perspectives on alcohol use disorder 1.1 The size of alcohol use disorder 1.1.1 Terminology of alcohol-use related disorders 1.1.2 Size and burden of alcohol consumption and alcohol use disorders 1.2 Cognitive psychological perspectives on alcohol use disorder 1.2.1 A unified framework for addiction 1.2.2 Value-based decision making 1.2.3 Goal-directed and habitual systems 1.3 Neurobiological perspectives on alcohol use disorders 1.3.1 Neural underpinnings of the reward circuit 1.3.2 Neural underpinning of goal-directed and habitual decision making 1.3.3 Striatal adaptations associated with chronic alcohol consumption 1.4 Synopsis and research questions Chapter 2. Study 1 2.1 Abstract 2.2 Introduction 2.3 Material and methods 2.3.1 Participants and procedure 2.3.2 Measures of goal-directed and habitual behavioral control 2.3.3 Measure of alcohol consumption 2.3.4 Behavioral statistical analyses 2.3.5 Functional magnetic resonance imaging data acquisition and analysis 2.4 Results 2.4.1 Sample characteristics 2.4.2 Behavioral results 2.4.3 Functional magnetic resonance imaging results 2.5 Discussion Chapter 3. Study 2 3.1 Abstract 3.2 Introduction 3.3 Methods and materials 3.3.1 Participants 3.3.2 Procedure 3.3.3 Alcohol Expectancy Questionnaire 3.3.4 Task 3.3.5 Magnetic Resonance Imaging 3.3.6 Follow-up procedure 3.3.7 Data analysis 3.3.8 fMRI analysis 3.4 Results 3.4.1 Sample characteristics 3.4.2 Task-related group differences 3.4.3 Interaction between alcohol expectancies and model-based control 3.4.4 fMRI results 3.5 Discussion Chapter 4. Study 3 4.1 Abstract 4.2 Introduction 4.3 Study 3.1 4.3.1 Material and methods 4.3.2 Results 4.4 Study 3.2 4.4.1 Material and methods 4.4.2 Results 4.5 Discussion Chapter 5. General discussion 5.1 Summary of findings and discussion 5.1.1 Goal-directed and habitual decision making and alcohol use (disorder) 5.1.2 Neuroimaging correlates of goal-directed and habitual control 5.1.3 Modulators of the valuation systems and alcohol use (disorders) 5.1.4 Integration of findings 5.2 Limitations 5.2.1 Methodological critique of the Two-Step task 5.3 Outlook for future studies 5.3.1 Tentative framework for future studies 5.4 Conclusions References Appendix A Supplementary Information of Study 1 A.1 Supplementary Methods 1 - behavioral A.2 Supplementary Methods 2 - fMRI A.3 Supplementary Results - behavioral A.4 Supplementary results - fMRI B Supplementary Information of Study 2 B.1 Computational fits B.2 Preprocessing of the functional imaging data B.3 Exclusion criteria for different analyses B.4 First level analysis of the functional imaging analysis B.5 Voxel-based morphometry B.6 Drinking Motives Questionnaire B.7 Model-free comparisons B.8 Association with time to relapse B.9 Number of detoxifications and model-based control: behavioral and neuroimaging analyses C Supplementary Information of Study 3 C.1 Differences between VBDM version used in this study compared to the VBDM version reported in Pooseh et al. (under review) C.2 Additional correlational analyses D Supplementary Information for additional analyses