Miriam Sebold1, Stephan Nebe2, Maria Garbusow3, Matthias Guggenmos3, Daniel J Schad4, Anne Beck3, Soeren Kuitunen-Paul5, Christian Sommer6, Robin Frank3, Peter Neu7, Ulrich S Zimmermann6, Michael A Rapp4, Michael N Smolka2, Quentin J M Huys8, Florian Schlagenhauf9, Andreas Heinz3. 1. Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany; Social and Preventive Medicine, Area of Excellence Cognitive Sciences, University of Potsdam, Potsdam, Germany. Electronic address: miriam.sebold@charite.de. 2. Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany; Neuroimaging Center, Technische Universität Dresden, Dresden, Germany. 3. Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany. 4. Social and Preventive Medicine, Area of Excellence Cognitive Sciences, University of Potsdam, Potsdam, Germany. 5. Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany. 6. Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany. 7. Jüdisches Krankenhaus Berlin, Berlin, Germany. 8. Translational Neuromodeling Unit, Department of Biomedical Engineering, Swiss Federal Institute of Technology (ETH) Zürich, University of Zürich, Zürich, Switzerland; Centre for Addictive Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zürich, Zürich, Switzerland. 9. Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Abstract
BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. METHODS: Ninety detoxified, medication-free, alcohol-dependent patients and 96 age- and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. RESULTS: Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. CONCLUSIONS: These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.
BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addictedpatients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. METHODS: Ninety detoxified, medication-free, alcohol-dependent patients and 96 age- and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. RESULTS:Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. CONCLUSIONS: These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.
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