@article{SekutowiczGuggenmosKuitunenPauletal.2019,
  author    = {Sekutowicz, Maria and Guggenmos, Matthias and Kuitunen-Paul, S{\"o}ren and Garbusow, Maria and Sebold, Miriam Hannah and Pelz, Patricia and Priller, Josef and Wittchen, Hans-Ulrich and Smolka, Michael N. and Zimmermann, Ulrich S. and Heinz, Andreas and Sterzer, Philipp and Schmack, Katharina},
  title     = {Neural Response Patterns During Pavlovian-to-Instrumental Transfer Predict Alcohol Relapse and Young Adult Drinking},
  series = {Biological psychiatry : a journal of psychiatric neuroscience and therapeutics ; a publication of the Society of Biological Psychiatry},
  volume    = {86},
  journal   = {Biological psychiatry : a journal of psychiatric neuroscience and therapeutics ; a publication of the Society of Biological Psychiatry},
  number    = {11},
  publisher = {Elsevier},
  address   = {New York},
  issn      = {0006-3223},
  doi       = {10.1016/j.biopsych.2019.06.028},
  pages     = {857 -- 863},
  year      = {2019},
  abstract  = {BACKGROUND: Pavlovian-to-instrumental transfer (PIT) describes the influence of conditioned stimuli on instrumental behaviors and is discussed as a key process underlying substance abuse. Here, we tested whether neural responses during alcohol-related PIT predict future relapse in alcohol-dependent patients and future drinking behavior in adolescents. METHODS: Recently detoxified alcohol-dependent patients (n = 52) and young adults without dependence (n = 136) underwent functional magnetic resonance imaging during an alcohol-related PIT paradigm, and their drinking behavior was assessed in a 12-month follow-up. To predict future drinking behavior from PIT activation patterns, we used a multivoxel classification scheme based on linear support vector machines. RESULTS: When training and testing the classification scheme in patients, PIT activation patterns predicted future relapse with 71.2\% accuracy. Feature selection revealed that classification was exclusively based on activation patterns in medial prefrontal cortex. To probe the generalizability of this functional magnetic resonance imaging-based prediction of future drinking behavior, we applied the support vector machine classifier that had been trained on patients to PIT functional magnetic resonance imaging data from adolescents. An analysis of cross-classification predictions revealed that those young social drinkers who were classified as abstainers showed a greater reduction in alcohol consumption at 12-month follow-up than those classified as relapsers (Delta = -24.4 +/- 6.0 g vs. -5.7 +/- 3.6 g; p = .019). CONCLUSIONS: These results suggest that neural responses during PIT could constitute a generalized prognostic marker for future drinking behavior in established alcohol use disorder and in at-risk states.},
  language  = {en}
}
@article{SeboldNebeGarbusowetal.2017,
  author    = {Sebold, Miriam Hannah and Nebe, Stephan and Garbusow, Maria and Guggenmos, Matthias and Schad, Daniel and Beck, Anne and Kuitunen-Paul, S{\"o}ren and Sommer, Christian and Frank, Robin and Neu, Peter and Zimmermann, Ulrich S. and Rapp, Michael Armin and Smolka, Michael N. and Huys, Quentin J. M. and Schlagenhauf, Florian and Heinz, Andreas},
  title     = {When Habits Are Dangerous: Alcohol Expectancies and Habitual Decision Making Predict Relapse in Alcohol Dependence},
  series = {Biological psychiatry : a journal of psychiatric neuroscience and therapeutics ; a publication of the Society of Biological Psychiatry},
  volume    = {82},
  journal   = {Biological psychiatry : a journal of psychiatric neuroscience and therapeutics ; a publication of the Society of Biological Psychiatry},
  publisher = {Elsevier},
  address   = {New York},
  issn      = {0006-3223},
  doi       = {10.1016/j.biopsych.2017.04.019},
  pages     = {847 -- 856},
  year      = {2017},
  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.},
  language  = {en}
}