Goal-directed and habitual decision making under stress in Gambling Disorder: an fMRI study

The development of addictive behaviors has been suggested to be related to a transition from goal-directed to habitual decision making. Stress is a factor known to prompt habitual behavior and to increase the risk for addiction and relapse. In the current study, we therefore used functional MRI to investigate the balance between goal-directed ‘model-based’ and habitual ‘model-free’ control systems and whether acute stress would differentially shift this balance in gambling disorder (GD) patients compared to healthy controls (HCs). Using a within-subject design, 22 patients with GD and 20 HCs underwent stress induction or a control condition before performing a multistep decision-making task during fMRI. Salivary cortisol levels showed that the stress induction was successful. Contrary to our hypothesis, GD patients showed intact goal-directed decision making, which remained similar to HCs after stress induction. Bayes factors provided substantial evidence against a difference between the groups or a group-by-stress interaction on the balance between model-based and model-free decision making. Similarly, neural estimates did not differ between groups and conditions. These results challenge the notion that GD is related to an increased reliance on habitual (or decreased goal-directed) control, even during stress

Imbalanced decision hierarchy in addicts emerging from drug-hijacked dopamine spiraling circuit

Despite explicitly wanting to quit, long-term addicts find themselves powerless to resist drugs, despite knowing that drug-taking may be a harmful course of action. Such inconsistency between the explicit knowledge of negative consequences and the compulsive behavioral patterns represents a cognitive/behavioral conflict that is a central characteristic of addiction. Neurobiologically, differential cue-induced activity in distinct striatal subregions, as well as the dopamine connectivity spiraling from ventral striatal regions to the dorsal regions, play critical roles in compulsive drug seeking. However, the functional mechanism that integrates these neuropharmacological observations with the above-mentioned cognitive/behavioral conflict is unknown. Here we provide a formal computational explanation for the drug-induced cognitive inconsistency that is apparent in the addicts' “self-described mistake”. We show that addictive drugs gradually produce a motivational bias toward drug-seeking at low-level habitual decision processes, despite the low abstract cognitive valuation of this behavior. This pathology emerges within the hierarchical reinforcement learning framework when chronic exposure to the drug pharmacologically produces pathologicaly persistent phasic dopamine signals. Thereby the drug hijacks the dopaminergic spirals that cascade the reinforcement signals down the ventro-dorsal cortico-striatal hierarchy. Neurobiologically, our theory accounts for rapid development of drug cue-elicited dopamine efflux in the ventral striatum and a delayed response in the dorsal striatum. Our theory also shows how this response pattern depends critically on the dopamine spiraling circuitry. Behaviorally, our framework explains gradual insensitivity of drug-seeking to drug-associated punishments, the blocking phenomenon for drug outcomes, and the persistent preference for drugs over natural rewards by addicts. The model suggests testable predictions and beyond that, sets the stage for a view of addiction as a pathology of hierarchical decision-making processes. This view is complementary to the traditional interpretation of addiction as interaction between habitual and goal-directed decision systems

Altered Risk-Based Decision Making following Adolescent Alcohol Use Results from an Imbalance in Reinforcement Learning in Rats

Alcohol use during adolescence has profound and enduring consequences on decision-making under risk. However, the fundamental psychological processes underlying these changes are unknown. Here, we show that alcohol use produces over-fast learning for better-than-expected, but not worse-than-expected, outcomes without altering subjective reward valuation. We constructed a simple reinforcement learning model to simulate altered decision making using behavioral parameters extracted from rats with a history of adolescent alcohol use. Remarkably, the learning imbalance alone was sufficient to simulate the divergence in choice behavior observed between these groups of animals. These findings identify a selective alteration in reinforcement learning following adolescent alcohol use that can account for a robust change in risk-based decision making persisting into later life

Compute to learn: Neural implementation of computations underlying associative learning and decision making

Contains fulltext : 545604.pdf (publisher's version ) (Open Access)Radboud University, 01 december 2016Promotores : Toni, I., Cools, R.179 p

Mechanisms Underlying Dopamine-Induced Risky Choice in Parkinson's Disease With and Without Depression (History)

Contains fulltext : 196637.pdf (publisher's version ) (Open Access)Patients with Parkinson's disease (PD) are often treated with dopaminergic medication. Dopaminergic medication is known to improve both motor and certain nonmotor symptoms, such as depression. However, it can contribute to behavioral impairment, for example, by enhancing risky choice. Here we characterize the computational mechanisms that contribute to dopamine-induced changes in risky choice in PD patients with and without a depression (history). We adopt a clinical-neuroeconomic approach to investigate the effects of dopaminergic medication on specific components of risky choice in PD. Twenty-three healthy controls, 21 PD patients with a depression (history), and 22 nondepressed PD patients were assessed using a well-established risky choice paradigm. Patients were tested twice: once after taking their normal dopaminergic medication and once after withdrawal of their medication. Dopaminergic medication increased a value-independent gambling propensity in nondepressed PD patients, while leaving loss aversion unaffected. By contrast, dopaminergic medication effects on loss aversion were associated with current depression severity and with drug effects on depression scores. The present findings demonstrate that dopaminergic medication increases a value-independent gambling bias in nondepressed PD patients. Moreover, the current study raises the hypothesis that dopamine-induced reductions in loss aversion might underlie previously observed comorbidity between depression and medication-related side effects in PD, such as impulse control disorder

Emotionally aversive cues suppress neural systems underlying optimal learning in socially anxious individuals

Learning and decision-making are modulated by socio-emotional processing and such modulation is implicated in clinically-relevant personality traits of social anxiety. The present study elucidates the computational and neural mechanisms by which emotionally aversive cues disrupt learning in socially anxious human individuals. Healthy volunteers with low or high trait social anxiety performed a reversal learning task requiring learning actions in response to angry or happy face cues. Choice data were best captured by a computational model in which learning rate was adjusted according to the history of surprises. High trait socially anxious individuals employed a less dynamic strategy for adjusting their learning rate in trials started with angry face cues and unlike the low social anxiety group, their dorsal anterior cingulate cortex (dACC) activity did not covary with the learning rate. Our results demonstrate that trait social anxiety is accompanied by disruption of optimal learning and dACC activity in threatening situations. Significance statement: Social anxiety is known to influence a broad range of cognitive functions. This study tests whether and how social anxiety affects human value-based learning as a function of uncertainty in the learning environment. The findings indicate that, in a threatening context evoked by an angry face, socially anxious individuals fail to benefit from a stable learning environment with highly predictable stimulus-response-outcome associations. Under those circumstances, socially anxious individuals failed to use their dorsal anterior cingulate cortex, a region known to adjust learning rate to environmental uncertainty. These findings open the way to modify neurobiological mechanisms of maladaptive learning in anxiety and depressive disorders

Neural Systems Underlying Optimal Learning in Socially Anxious Individuals

Item does not contain fulltextLearning and decision-making are modulated by socio-emotional processing and such modulation is implicated in clinically relevantpersonality traits of social anxiety. The present study elucidates the computational and neural mechanisms by which emotionallyaversive cues disrupt learning in socially anxious human individuals. Healthy volunteers with low or high trait social anxiety performeda reversal learning task requiring learning actions in response to angry or happy face cues. Choice data were best captured by a computationalmodel in which learning rate was adjusted according to the history of surprises. High trait socially anxious individuals used aless-dynamic strategy for adjusting their learning rate in trials started with angry face cues and unlike the low social anxiety group, theirdorsal anterior cingulate cortex (dACC) activity did not covary with the learning rate. Our results demonstrate that trait social anxiety isaccompanied by disruption of optimal learning and dACC activity in threatening situations.nul

Effects of monensin supplementation on lactation performance of dairy cows: a systematic review and dose–response meta‑analysis

The aim of this study was to conduct a comprehensive review with meta-analysis to determine the effects of the dose-response relationship between monensin supplementation and dairy cow performance and milk composition. Results from 566 full-text articles and 48 articles with 52 studies were meta-analyzed for pooled estimates. Monensin supplementation up to 23 ppm increased milk production, with the optimal dose being 12.6 ppm. Monensin supplementation at doses ranging from 16 to 96 ppm increased milk production in the prepartum phase (- 28 to 0 day relative to calving). From 60 to 150 DIM, monensin supplementation up to 21 ppm had a significant positive effect on this outcome, while supplementation in the 37 to 96 ppm range caused a decrease in this variable. At 0 to 60 and > 150 DIM, monensin supplementation had no effect on milk yield. At dosages of 22 to 96 ppm, 12 to 36 ppm, and below 58 ppm and 35 ppm, respectively, monensin supplementation resulted in significant decreases in dry matter intake (DMI), milk protein percentage, milk fat percentage, and milk fat yield. Overall, based on the results of this meta-analysis and considering all variables, the recommended optimal dose of monensin could be about 16 ppm

Effects of monensin supplementation on lactation performance of dairy cows: a systematic review and dose–response meta‑analysis

Abstract The aim of this study was to conduct a comprehensive review with meta-analysis to determine the effects of the dose–response relationship between monensin supplementation and dairy cow performance and milk composition. Results from 566 full-text articles and 48 articles with 52 studies were meta-analyzed for pooled estimates. Monensin supplementation up to 23 ppm increased milk production, with the optimal dose being 12.6 ppm. Monensin supplementation at doses ranging from 16 to 96 ppm increased milk production in the prepartum phase (− 28 to 0 day relative to calving). From 60 to 150 DIM, monensin supplementation up to 21 ppm had a significant positive effect on this outcome, while supplementation in the 37 to 96 ppm range caused a decrease in this variable. At 0 to 60 and > 150 DIM, monensin supplementation had no effect on milk yield. At dosages of 22 to 96 ppm, 12 to 36 ppm, and below 58 ppm and 35 ppm, respectively, monensin supplementation resulted in significant decreases in dry matter intake (DMI), milk protein percentage, milk fat percentage, and milk fat yield. Overall, based on the results of this meta-analysis and considering all variables, the recommended optimal dose of monensin could be about 16 ppm