BackgroundAlcohol use disorder is characterized by perseverative alcohol use despite negative consequences. This hallmark feature of addiction potentially relates to impairments in behavioral flexibility, which can be measured by probabilistic reversal learning (PRL) paradigms. We here aimed to examine the cognitive mechanisms underlying impaired PRL task performance in patients with alcohol use disorder (AUDP) using computational models of reinforcement learning.MethodsTwenty-eight early abstinent AUDP and 27 healthy controls (HC) performed an extensive PRL paradigm. We compared conventional behavioral variables of choices (perseveration; correct responses) between groups. Moreover, we fitted Bayesian computational models to the task data to compare differences in latent cognitive variables including reward and punishment learning and choice consistency between groups.ResultsAUDP and HC did not significantly differ with regard to direct perseveration rates after reversals. However, AUDP made overall less correct responses and specifically showed decreased win–stay behavior compared to HC. Interestingly, AUDP showed premature switching after no or little negative feedback but elevated proneness to stay when accumulation of negative feedback would make switching a more optimal option. Computational modeling revealed that AUDP compared to HC showed enhanced learning from punishment, a tendency to learn less from positive feedback and lower choice consistency.ConclusionOur data do not support the assumption that AUDP are characterized by increased perseveration behavior. Instead our findings provide evidence that enhanced negative reinforcement and decreased non-drug-related reward learning as well as diminished choice consistency underlie dysfunctional choice behavior in AUDP.
Introduction: Bipolar disorder (BD) is a significant mental health concern in the world. Most of the drugs used in the treatment of bipolar disease may cause life-threatening arrhythmias by affecting the distribution of ventricular repolarization (VR). VR is commonly assessed using QT interval and Twave measurements. The aim of this study was to evaluate VR using the T peak to T end (Tp-e) interval and the Tp-e/QT ratio and to investigate the relationship between lithium therapy and VR parameters in patients with BD. Patients and Methods: Forty-six BD patients under lithium therapy and 45 participants in a control group were included in our study. The Tp-e interval and Tp-e/QT ratio were measured using 12derivation electrocardiography (ECG). These parameters were compared between groups. Results: The QT interval (p= 0.01), QTc interval (p= 0.003), Tp-e interval (p< 0.001), and Tpe/QT ratio (p= 0.009) were significantly higher in patients with BD than in the control group. There was a positive correlation between the Tp-e interval and serum lithium levels (r= 0.317, p< 0.032). In addition, the increased serum lithium level (β= 0.398, p= 0.007) was found to be an independent predictor of the prolonged Tp-e interval. Conclusion: Prolonged Tp-e interval may be a useful indicator of increased risk of ventricular arrhythmia in patients using lithium therapy.
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