Chronic alcohol intake abolishes the relationship between dopamine synthesis capacity and learning signals in the ventral striatum

Impaired flexible reward learning in ADHD patients is associated with blunted reinforcement sensitivity and neural signals in ventral striatum and parietal cortex

Reward-based learning and decision-making are prime candidates to understand symptoms of attention deficit hyperactivity disorder (ADHD). However, only limited evidence is available regarding the neurocomputational underpinnings of the alterations seen in ADHD. This concerns flexible behavioral adaption in dynamically changing environments, which is challenging for individuals with ADHD. One previous study points to elevated choice switching in adolescent ADHD, which was accompanied by disrupted learning signals in medial prefrontal cortex. Here, we investigated young adults with ADHD (n = 17) as compared to age- and sex-matched controls (n = 17) using a probabilistic reversal learning experiment during functional magnetic resonance imaging (fMRI). The task requires continuous learning to guide flexible behavioral adaptation to changing reward contingencies. To disentangle the neurocomputational underpinnings of the behavioral data, we used reinforcement learning (RL) models, which informed the analysis of fMRI data. ADHD patients performed worse than controls particularly in trials before reversals, i.e., when reward contingencies were stable. This pattern resulted from 'noisy' choice switching regardless of previous feedback. RL modelling showed decreased reinforcement sensitivity and enhanced learning rates for negative feedback in ADHD patients. At the neural level, this was reflected in a diminished representation of choice probability in the left posterior parietal cortex in ADHD. Moreover, modelling showed a marginal reduction of learning about the unchosen option, which was paralleled by a marginal reduction in learning signals incorporating the unchosen option in the left ventral striatum. Taken together, we show that impaired flexible behavior in ADHD is due to excessive choice switching ('hyper-flexibility'), which can be detrimental or beneficial depending on the learning environment. Computationally, this resulted from blunted sensitivity to reinforcement of which we detected neural correlates in the attention-control network, specifically in the parietal cortex. These neurocomputational findings remain preliminary due to the relatively small sample size.

Reward prediction-errors weighted by cue salience produces addictive behaviours in simulations, with asymmetrical learning and steeper delay discounting

Dysfunction in learning and motivational systems are thought to contribute to addictive behaviours. Previous models have suggested that dopaminergic roles in learning and motivation could produce addictive behaviours through pharmacological manipulations that provide excess dopaminergic signalling towards these learning and motivational systems. Redish (2004) suggested a role based on dopaminergic signals of value prediction error, while (Zhang et al., 2009) suggested a role based on dopaminergic signals of motivation. However, both models present significant limitations. They do not explain the reduced sensitivity to drug-related costs/negative consequences, the increased impulsivity generally found in people with a substance use disorder, craving behaviours, and non-pharmacological dependence, all of which are key hallmarks of addictive behaviours. Here, we propose a novel mathematical definition of salience, that combines aspects of dopamine's role in both learning and motivation within the reinforcement learning framework. Using a single parameter regime, we simulated addictive behaviours that the (Zhang et al., 2009; Redish, 2004) models also produce but we went further in simulating the downweighting of drug-related negative prediction-errors, steeper delay discounting of drug rewards, craving behaviours and aspects of behavioural/non-pharmacological addictions. The current salience model builds on our recently proposed conceptual theory that salience modulates internal representation updating and may contribute to addictive behaviours by producing misaligned internal representations (Kalhan et al., 2021). Critically, our current mathematical model of salience argues that the seemingly disparate learning and motivational aspects of dopaminergic functioning may interact through a salience mechanism that modulates internal representation updating.

Blunted reward prediction error signals in internet gaming disorder

BACKGROUND

Internet gaming disorder (IGD) is a type of behavioural addictions. One of the key features of addiction is the excessive exposure to addictive objectives (e.g. drugs) reduces the sensitivity of the brain reward system to daily rewards (e.g. money). This is thought to be mediated via the signals expressed as dopaminergic reward prediction error (RPE). Emerging evidence highlights blunted RPE signals in drug addictions. However, no study has examined whether IGD also involves alterations in RPE signals that are observed in other types of addictions.

METHODS

To fill this gap, we used functional magnetic resonance imaging data from 45 IGD and 42 healthy controls (HCs) during a reward-related prediction-error task and utilised a psychophysiological interaction (PPI) analysis to characterise the underlying neural correlates of RPE and related functional connectivity.

RESULTS

Relative to HCs, IGD individuals showed impaired reinforcement learning, blunted RPE signals in multiple regions of the brain reward system, including the right caudate, left orbitofrontal cortex (OFC), and right dorsolateral prefrontal cortex (DLPFC). Moreover, the PPI analysis revealed a pattern of hyperconnectivity between the right caudate, right putamen, bilateral DLPFC, and right dorsal anterior cingulate cortex (dACC) in the IGD group. Finally, linear regression suggested that the connection between the right DLPFC and right dACC could significantly predict the variation of RPE signals in the left OFC.

CONCLUSIONS

These results highlight disrupted RPE signalling and hyperconnectivity between regions of the brain reward system in IGD. Reinforcement learning deficits may be crucial underlying characteristics of IGD pathophysiology.

Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder

BACKGROUND

Alcohol 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.

METHODS

Twenty-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.

RESULTS

AUDP 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.

CONCLUSION

Our 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.

Decreased information processing speed and decision-making performance in alcohol use disorder: combined neurostructural evidence from VBM and TBSS

Alcohol Use Disorder (AUD) is a chronic relapsing condition characterized by excessive alcohol consumption despite its multifaceted adverse consequences, associated with impaired performance in several cognitive domains including decision-making. While choice deficits represent a core component of addictive behavior, possibly consecutive to brain changes preceding the onset of the addiction cycle, the evidence on grey-matter and white-matter damage underlying abnormal choices in AUD is still limited. To fill this gap, we assessed the neurostructural bases of decision-making performance in 22 early-abstinent alcoholic patients and 18 controls, by coupling the Cambridge Gambling Task (CGT) with quantitative magnetic resonance imaging metrics of grey-matter density and white-matter integrity. Regardless of group, voxel based morphometry highlighted an inverse relationship between deliberation time and grey-matter density, with alcoholics displaying slower choices related to grey-matter atrophy in key nodes of the motor control network. In particular, grey-matter density in the supplementary motor area, reduced in alcoholic patients, explained a significant amount of variability in their increased deliberation time. Tract-based spatial statistics revealed a significant relationship between CGT deliberation time and all white-matter indices, involving the most relevant commissural, projection and associative tracts. The lack of choice impairments other than increased deliberation time highlights reduced processing speed, mediated both by grey-matter and white-matter alterations, as a possible marker of a generalized executive impairment extending to the output stages of decision-making. These results pave the way to further studies aiming to tailor novel rehabilitation strategies and assess their functional outcomes.

Computational theory-driven studies of reinforcement learning and decision-making in addiction: What have we learned?

Computational psychiatry provides a powerful new approach for linking the behavioral manifestations of addiction to their precise cognitive and neurobiological substrates. However, this emerging area of research is still limited in important ways. While research has identified features of reinforcement learning and decision-making in substance users that differ from health, less emphasis has been placed on capturing addiction cycles/states dynamically, within-person. In addition, the focus on few behavioral variables at a time has precluded more detailed consideration of related processes and heterogeneous clinical profiles. We propose that a longitudinal and multidimensional examination of value-based processes, a type of dynamic "computational fingerprint", will provide a more complete understanding of addiction as well as aid in developing better tailored and timed interventions.

Common and separable neural alterations in substance use disorders: A coordinate-based meta-analyses of functional neuroimaging studies in humans

Delineating common and separable neural alterations in substance use disorders (SUD) is imperative to understand the neurobiological basis of the addictive process and to inform substance‐specific treatment strategies. Given numerous functional MRI (fMRI) studies in different SUDs, a meta‐analysis could provide an opportunity to determine robust shared and substance‐specific alterations. The present study employed a coordinate‐based meta‐analysis covering fMRI studies in individuals with addictive cocaine, cannabis, alcohol, and nicotine use. The primary meta‐analysis demonstrated common alterations in primary dorsal striatal, and frontal circuits engaged in reward/salience processing, habit formation, and executive control across different substances and task‐paradigms. Subsequent sub‐analyses revealed substance‐specific alterations in frontal and limbic regions, with marked frontal and insula‐thalamic alterations in alcohol and nicotine use disorders respectively. Examining task‐specific alterations across substances revealed pronounced frontal alterations during cognitive processes yet stronger striatal alterations during reward‐related processes. Finally, an exploratory meta‐analysis revealed that neurofunctional alterations in striatal and frontal reward processing regions can already be determined with a high probability in studies with subjects with comparably short durations of use. Together the findings emphasize the role of dysregulations in frontostriatal circuits and dissociable contributions of these systems in the domains of reward‐related and cognitive processes which may contribute to substance‐specific behavioral alterations.

Addictions NeuroImaging Assessment (ANIA): Towards an integrative framework for alcohol use disorder

Alcohol misuse and addiction are major international public health issues. Addiction can be characterized as a disorder of aberrant neurocircuitry interacting with environmental, genetic and social factors. Neuroimaging in alcohol misuse can thus provide a critical window into underlying neural mechanisms, highlighting possible treatment targets and acting as clinical biomarkers for predicting risk and treatment outcomes. This neuroimaging review on alcohol misuse in humans follows the Addictions Neuroclinical Assessment (ANA) that proposes incorporating three functional neuroscience domains integral to the neurocircuitry of addiction: incentive salience and habits, negative emotional states, and executive function within the context of the addiction cycle. Here we review and integrate multiple imaging modalities focusing on underlying cognitive processes such as reward anticipation, negative emotionality, cue reactivity, impulsivity, compulsivity and executive function. We highlight limitations in the literature and propose a model forward in the use of neuroimaging as a tool to understanding underlying mechanisms and potential clinical applicability for phenotyping of heterogeneity and predicting risk and treatment outcomes.

Pavlovian-To-Instrumental Transfer and Alcohol Consumption in Young Male Social Drinkers: Behavioral, Neural and Polygenic Correlates

In animals and humans, behavior can be influenced by irrelevant stimuli, a phenomenon called Pavlovian-to-instrumental transfer (PIT). In subjects with substance use disorder, PIT is even enhanced with functional activation in the nucleus accumbens (NAcc) and amygdala. While we observed enhanced behavioral and neural PIT effects in alcohol-dependent subjects, we here aimed to determine whether behavioral PIT is enhanced in young men with high-risk compared to low-risk drinking and subsequently related functional activation in an a-priori region of interest encompassing the NAcc and amygdala and related to polygenic risk for alcohol consumption. A representative sample of 18-year old men (n = 1937) was contacted: 445 were screened, 209 assessed: resulting in 191 valid behavioral, 139 imaging and 157 genetic datasets. None of the subjects fulfilled criteria for alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders-IV-TextRevision (DSM-IV-TR). We measured how instrumental responding for rewards was influenced by background Pavlovian conditioned stimuli predicting action-independent rewards and losses. Behavioral PIT was enhanced in high-compared to low-risk drinkers (b = 0.09, SE = 0.03, z = 2.7, p < 0.009). Across all subjects, we observed PIT-related neural blood oxygen level-dependent (BOLD) signal in the right amygdala (t = 3.25, p_SVC = 0.04, x = 26, y = -6, z = -12), but not in NAcc. The strength of the behavioral PIT effect was positively correlated with polygenic risk for alcohol consumption (r_s = 0.17, p = 0.032). We conclude that behavioral PIT and polygenic risk for alcohol consumption might be a biomarker for a subclinical phenotype of risky alcohol consumption, even if no drug-related stimulus is present. The association between behavioral PIT effects and the amygdala might point to habitual processes related to out PIT task. In non-dependent young social drinkers, the amygdala rather than the NAcc is activated during PIT; possible different involvement in association with disease trajectory should be investigated in future studies.

Explanatory integration, computational phenotypes, and dimensional psychiatry: The case of alcohol use disorder

We compare three theoretical frameworks for pursuing explanatory integration in psychiatry: a new dimensional framework grounded in the notion of computational phenotype, a mechanistic framework, and a network of symptoms framework. Considering the phenomenon of alcoholism, we argue that the dimensional framework is the best for effectively integrating computational and mechanistic explanations with phenomenological analyses.

Accelerated alcohol use across adolescence predicts early adult symptoms of alcohol use disorder via reward-related neural function

BACKGROUND

Alcohol use is commonly initiated during adolescence, with earlier onset known to increase the risk for alcohol use disorder (AUD). Altered function in neural reward circuitry is thought to increase the risk for AUD. To test the hypothesis that adolescent alcohol misuse primes the brain for alcohol-related psychopathology in early adulthood, we examined whether adolescent alcohol consumption rates predicted reward responsivity in the ventral striatum (VS), and in turn, AUD symptoms in adulthood.

METHODS

A total of 139 low income, racially diverse urban males reported on their alcohol use at ages 11, 12, 15, and 17; completed self-reports of personality, psychiatric interviews, and a functional magnetic resonance imaging (fMRI) scan at age 20; and completed a psychiatric interview at age 22. We measured adolescent alcohol use trajectories using latent growth curve modeling and measured neural responses to monetary reward using a VS region of interest. We tested indirect effects of adolescent alcohol use on AUD symptoms at age 22 via VS reward-related reactivity at age 20.

RESULTS

Greater acceleration in adolescent alcohol use predicted increased VS response during reward anticipation at age 20. VS reactivity to reward anticipation at age 20 predicted AUD symptoms at age 22, over and above concurrent symptoms. Accelerated adolescent alcohol use predicted AUD symptoms in early adulthood via greater VS reactivity to reward anticipation.

CONCLUSIONS

Prospective findings support a pathway through which adolescent alcohol use increases the risk for AUD in early adulthood by impacting reward-related neural functioning. These results highlight increased VS reward-related reactivity as a biomarker for AUD vulnerability.

Increased Striatal Dopamine Synthesis Capacity in Gambling Addiction

BACKGROUND

The hypothesis that dopamine plays an important role in the pathophysiology of pathological gambling is pervasive. However, there is little to no direct evidence for a categorical difference between pathological gamblers and healthy control subjects in terms of dopamine transmission in a drug-free state. Here we provide evidence for this hypothesis by comparing dopamine synthesis capacity in the dorsal and ventral parts of the striatum in 13 pathological gamblers and 15 healthy control subjects.

METHODS

This was achieved using [¹⁸F]fluoro-levo-dihydroxyphenylalanine dynamic positron emission tomography scans and striatal regions of interest that were hand-drawn based on visual inspection of individual structural magnetic resonance imaging scans.

RESULTS

Our results show that dopamine synthesis capacity was increased in pathological gamblers compared with healthy control subjects. Dopamine synthesis was 16% higher in the caudate body, 17% higher in the dorsal putamen, and 17% higher in the ventral striatum in pathological gamblers compared with control subjects. Moreover, dopamine synthesis capacity in the dorsal putamen and caudate head was positively correlated with gambling distortions in pathological gamblers.

CONCLUSIONS

Taken together, these results provide empirical evidence for increased striatal dopamine synthesis in pathological gambling.

A Focus on Reward Prediction and the Lateral Habenula: Functional Alterations and the Behavioral Outcomes Induced by Drugs of Abuse

The lateral habenula (LHb) regulates reward learning and controls the updating of reward-related information. Drugs of abuse have the capacity to hijack the cellular and neurocircuit mechanisms mediating reward learning, forming non-adaptable, compulsive behaviors geared toward obtaining illicit substances. Here, we discuss current findings demonstrating how drugs of abuse alter intrinsic and synaptic LHb neuronal function. Additionally, we discuss evidence for how drug-induced LHb alterations may affect the ability to predict reward, potentially facilitating an addiction-like state. Altogether, we combine ex vivo and in vivo results for an overview of how drugs of abuse alter LHb function and how these functional alterations affect the ability to learn and update behavioral responses to hedonic external stimuli.

The alcoholic brain: neural bases of impaired reward-based decision-making in alcohol use disorders

Neuroeconomics is providing insights into the neural bases of decision-making in normal and pathological conditions. In the neuropsychiatric domain, this discipline investigates how abnormal functioning of neural systems associated with reward processing and cognitive control promotes different disorders, and whether such evidence may inform treatments. This endeavor is crucial when studying different types of addiction, which share a core promoting mechanism in the imbalance between impulsive subcortical neural signals associated with immediate pleasurable outcomes and inhibitory signals mediated by a prefrontal reflective system. The resulting impairment in behavioral control represents a hallmark of alcohol use disorders (AUDs), a chronic relapsing disorder characterized by excessive alcohol consumption despite devastating consequences. This review aims to summarize available magnetic resonance imaging (MRI) evidence on reward-related decision-making alterations in AUDs, and to envision possible future research directions. We review functional MRI (fMRI) studies using tasks involving monetary rewards, as well as MRI studies relating decision-making parameters to neurostructural gray- or white-matter metrics. The available data suggest that excessive alcohol exposure affects neural signaling within brain networks underlying adaptive behavioral learning via the implementation of prediction errors. Namely, weaker ventromedial prefrontal cortex activity and altered connectivity between ventral striatum and dorsolateral prefrontal cortex likely underpin a shift from goal-directed to habitual actions which, in turn, might underpin compulsive alcohol consumption and relapsing episodes despite adverse consequences. Overall, these data highlight abnormal fronto-striatal connectivity as a candidate neurobiological marker of impaired choice in AUDs. Further studies are needed, however, to unveil its implications in the multiple facets of decision-making.

Behavioral and Neural Signatures of Reduced Updating of Alternative Options in Alcohol-Dependent Patients during Flexible Decision-Making

Addicted individuals continue substance use despite the knowledge of harmful consequences and often report having no choice but to consume. Computational psychiatry accounts have linked this clinical observation to difficulties in making flexible and goal-directed decisions in dynamic environments via consideration of potential alternative choices. To probe this in alcohol-dependent patients (n = 43) versus healthy volunteers (n = 35), human participants performed an anticorrelated decision-making task during functional neuroimaging. Via computational modeling, we investigated behavioral and neural signatures of inference regarding the alternative option. While healthy control subjects exploited the anticorrelated structure of the task to guide decision-making, alcohol-dependent patients were relatively better explained by a model-free strategy due to reduced inference on the alternative option after punishment. Whereas model-free prediction error signals were preserved, alcohol-dependent patients exhibited blunted medial prefrontal signatures of inference on the alternative option. This reduction was associated with patients' behavioral deficit in updating the alternative choice option and their obsessive-compulsive drinking habits. All results remained significant when adjusting for potential confounders (e.g., neuropsychological measures and gray matter density). A disturbed integration of alternative choice options implemented by the medial prefrontal cortex appears to be one important explanation for the puzzling question of why addicted individuals continue drug consumption despite negative consequences.

SIGNIFICANCE STATEMENT

In addiction, patients maintain substance use despite devastating consequences and often report having no choice but to consume. These clinical observations have been theoretically linked to disturbed mechanisms of inference, for example, to difficulties when learning statistical regularities of the environmental structure to guide decisions. Using computational modeling, we demonstrate disturbed inference on alternative choice options in alcohol addiction. Patients neglecting "what might have happened" was accompanied by blunted coding of inference regarding alternative choice options in the medial prefrontal cortex. An impaired integration of alternative choice options implemented by the medial prefrontal cortex might contribute to ongoing drug consumption in the face of evident negative consequences.

Neural Signatures of Cognitive Flexibility and Reward Sensitivity Following Nicotinic Receptor Stimulation in Dependent Smokers: A Randomized Trial

IMPORTANCE

Withdrawal from nicotine is an important contributor to smoking relapse. Understanding how reward-based decision making is affected by abstinence and by pharmacotherapies such as nicotine replacement therapy and varenicline tartrate may aid cessation treatment.

OBJECTIVE

To independently assess the effects of nicotine dependence and stimulation of the nicotinic acetylcholine receptor on the ability to interpret valence information (reward sensitivity) and subsequently alter behavior as reward contingencies change (cognitive flexibility) in a probabilistic reversal learning task.

DESIGN, SETTING, AND PARTICIPANTS

Nicotine-dependent smokers and nonsmokers completed a probabilistic reversal learning task during acquisition of functional magnetic resonance imaging (fMRI) in a 2-drug, double-blind placebo-controlled crossover design conducted from January 21, 2009, to September 29, 2011. Smokers were abstinent from cigarette smoking for 12 hours for all sessions. In a fully Latin square fashion, participants in both groups underwent MRI twice while receiving varenicline and twice while receiving a placebo pill, wearing either a nicotine or a placebo patch. Imaging analysis was performed from June 15, 2015, to August 10, 2016.

MAIN OUTCOME AND MEASURES

A well-established computational model captured effects of smoking status and administration of nicotine and varenicline on probabilistic reversal learning choice behavior. Neural effects of smoking status, nicotine, and varenicline were tested for on MRI contrasts that captured reward sensitivity and cognitive flexibility.

RESULTS

The study included 24 nicotine-dependent smokers (12 women and 12 men; mean [SD] age, 35.8 [9.9] years) and 20 nonsmokers (10 women and 10 men; mean [SD] age, 30.4 [7.2] years). Computational modeling indicated that abstinent smokers were biased toward response shifting and that their decisions were less sensitive to the available evidence, suggesting increased impulsivity during withdrawal. These behavioral impairments were mitigated with nicotine and varenicline. Similarly, decreased mesocorticolimbic activity associated with cognitive flexibility in abstinent smokers was restored to the level of nonsmokers following stimulation of nicotinic acetylcholine receptors (familywise error-corrected P < .05). Conversely, neural signatures of decreased reward sensitivity in smokers (vs nonsmokers; familywise error-corrected P < .05) in the dorsal striatum and anterior cingulate cortex were not mitigated by nicotine or varenicline.

CONCLUSIONS AND RELEVANCE

There was a double dissociation between the effects of chronic nicotine dependence on neural representations of reward sensitivity and acute effects of stimulation of nicotinic acetylcholine receptors on behavioral and neural signatures of cognitive flexibility in smokers. These chronic and acute pharmacologic effects were observed in overlapping mesocorticolimbic regions, suggesting that available pharmacotherapies may alleviate deficits in the same circuitry for certain mental computations but not for others.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT00830739.

Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging

PURPOSE OF REVIEW

Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease.

RECENT FINDINGS

By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

Dorsolateral prefrontal cortex contributes to the impaired behavioral adaptation in alcohol dependence

Substance-dependent individuals often lack the ability to adjust decisions flexibly in response to the changes in reward contingencies. Prediction errors (PEs) are thought to mediate flexible decision-making by updating the reward values associated with available actions. In this study, we explored whether the neurobiological correlates of PEs are altered in alcohol dependence. Behavioral, and functional magnetic resonance imaging (fMRI) data were simultaneously acquired from 34 abstinent alcohol-dependent patients (ADP) and 26 healthy controls (HC) during a probabilistic reward-guided decision-making task with dynamically changing reinforcement contingencies. A hierarchical Bayesian inference method was used to fit and compare learning models with different assumptions about the amount of task-related information subjects may have inferred during the experiment. Here, we observed that the best-fitting model was a modified Rescorla-Wagner type model, the “double-update” model, which assumes that subjects infer the knowledge that reward contingencies are anti-correlated, and integrate both actual and hypothetical outcomes into their decisions. Moreover, comparison of the best-fitting model's parameters showed that ADP were less sensitive to punishments compared to HC. Hence, decisions of ADP after punishments were loosely coupled with the expected reward values assigned to them. A correlation analysis between the model-generated PEs and the fMRI data revealed a reduced association between these PEs and the BOLD activity in the dorsolateral prefrontal cortex (DLPFC) of ADP. A hemispheric asymmetry was observed in the DLPFC when positive and negative PE signals were analyzed separately. The right DLPFC activity in ADP showed a reduced correlation with positive PEs. On the other hand, ADP, particularly the patients with high dependence severity, recruited the left DLPFC to a lesser extent than HC for processing negative PE signals. These results suggest that the DLPFC, which has been linked to adaptive control of action selection, may play an important role in cognitive inflexibility observed in alcohol dependence when reinforcement contingencies change. Particularly, the left DLPFC may contribute to this impaired behavioral adaptation, possibly by impeding the extinction of the actions that no longer lead to a reward.

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Alcohol-dependent patients (ADP) had difficulty adapting to the reversals.

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The impaired adaptation was associated with a decrease in punishment sensitivity.

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The dorsolateral prefrontal cortex (DLPFC) of ADP failed to track prediction errors.

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A reduced tracking of the negative prediction error was present in the left DLPFC.

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The clinical severity of dependence was correlated with abnormal DLPFC activity.

Translating the Habenula-From Rodents to Humans

The habenula (Hb) is a central structure connecting forebrain to midbrain regions. This microstructure regulates monoaminergic systems, notably dopamine and serotonin, and integrates cognitive with emotional and sensory processing. Early preclinical data have described Hb as a brain nucleus activated in anticipation of aversive outcomes. Evidence has now accumulated to show that the Hb encodes both rewarding and aversive aspects of external stimuli, thus driving motivated behaviors and decision making. Human Hb research is still nascent but develops rapidly, alongside with the growth of neuroimaging and deep brain stimulation techniques. Not surprisingly, Hb dysfunction has been associated with psychiatric disorders, and studies in patients have established evidence for Hb involvement in major depression, addiction, and schizophrenia, as well as in pain and analgesia. Here, we summarize current knowledge from animal research and overview the existing human literature on anatomy and function of the Hb. We also discuss challenges and future directions in targeting this small brain structure in both rodents and humans. By combining animal data and human experimental studies, this review addresses the translational potential of preclinical Hb research.

Hat Computational Psychiatry Relevanz für die klinische Praxis der Psychiatrie?

Zusammenfassung. Computational Psychiatry (CP) ist ein junges Wissenschaftsfeld an der Schnittstelle zwischen der klinischen Psychiatrie und den mathematischen Neurowissenschaften, das sich in den letzten Jahren zu entfalten begonnen hat. Dieser Artikel widmet sich den möglichen klinischen Implikationen dieser jungen Disziplin. Wir (i) beginnen mit einer kurzen Übersicht über die Geschichte, Ziele und Inhalte der CP, (ii) beschreiben die zentralen Themen, Modelle und Theorien der CP, (iii) untersuchen die Relevanz und das Potenzial modell-basierter diagnostischer Tests (computational assays) für die Lösung zentraler Probleme in der klinischen Psychiatrie, und (iv) stellen zukünftige Herausforderungen und Chancen der CP dar.

A review of positron emission tomography studies exploring the dopaminergic system in substance use with a focus on tobacco as a co-variate

With the evolving sensitivity of positron emission tomography (PET) and the emergence of novel radiotracers, greater insight has been gained into the dopaminergic system as it relates to substance use. In this review, we summarize PET investigations from the last ten years that explore the dopaminergic system in tobacco, alcohol, stimulant, opiates, and cannabis addiction. In light of the prevalence of substance co-use, this review will also explore the effect of tobacco and other substance abuse co-morbidity on the dopaminergic system across study samples in the reviewed literature. In non-dependence, increased DA transmission following acute stimulant administration is a robust and consistent observation but is less detectable following acute alcohol and tobacco, where it likely represents a conditioned effect mediating reward expectation. Chronic drug exposure is generally associated with a hypo-functioning pre-synaptic dopamine system and lower D2/D3 receptor availability relative to healthy controls. Emerging evidence also shows that stimulant use disorders in particular may also be associated with greater D3 receptor availability relative to controls. A defined role for the dopaminergic system in cannabis and opiate use is yet to be elucidated. Future work is also needed to delineate the potential interactive effects of acute and chronic tobacco and substance co-use on the dopaminergic system.

PET imaging for addiction medicine: From neural mechanisms to clinical considerations

Positron emission tomography (PET) has been shown to be an effective imaging technique to study neurometabolic and neurochemical processes involved in addiction. That is, PET has been used to research neurobiological differences in substance abusers versus healthy controls and the pharmacokinetics and pharmacodynamics of abused drugs. Over the past years, the research scope has shifted to investigating neurobiological effects of abstinence and treatment, and their predictive power for relapse and other clinical outcomes. This chapter provides an overview of PET methodology, recent human PET studies on drug addiction and their implications for clinical treatment.

Lateral prefrontal model-based signatures are reduced in healthy individuals with high trait impulsivity

High impulsivity is an important risk factor for addiction with evidence from endophenotype studies. In addiction, behavioral control is shifted toward the habitual end. Habitual control can be described by retrospective updating of reward expectations in 'model-free' temporal-difference algorithms. Goal-directed control relies on the prospective consideration of actions and their outcomes, which can be captured by forward-planning 'model-based' algorithms. So far, no studies have examined behavioral and neural signatures of model-free and model-based control in healthy high-impulsive individuals. Fifty healthy participants were drawn from the upper and lower ends of 452 individuals, completing the Barratt Impulsiveness Scale. All participants performed a sequential decision-making task during functional magnetic resonance imaging (fMRI) and underwent structural MRI. Behavioral and fMRI data were analyzed by means of computational algorithms reflecting model-free and model-based control. Both groups did not differ regarding the balance of model-free and model-based control, but high-impulsive individuals showed a subtle but significant accentuation of model-free control alone. Right lateral prefrontal model-based signatures were reduced in high-impulsive individuals. Effects of smoking, drinking, general cognition or gray matter density did not account for the findings. Irrespectively of impulsivity, gray matter density in the left dorsolateral prefrontal cortex was positively associated with model-based control. The present study supports the idea that high levels of impulsivity are accompanied by behavioral and neural signatures in favor of model-free behavioral control. Behavioral results in healthy high-impulsive individuals were qualitatively different to findings in patients with the same task. The predictive relevance of these results remains an important target for future longitudinal studies.

Prefrontal and Striatal Glutamate Differently Relate to Striatal Dopamine: Potential Regulatory Mechanisms of Striatal Presynaptic Dopamine Function?

Theoretical and animal work has proposed that prefrontal cortex (PFC) glutamate inhibits dopaminergic inputs to the ventral striatum (VS) indirectly, whereas direct VS glutamatergic afferents have been suggested to enhance dopaminergic inputs to the VS. In the present study, we aimed to investigate relationships of glutamate and dopamine measures in prefrontostriatal circuitries of healthy humans. We hypothesized that PFC and VS glutamate, as well as their balance, are differently associated with VS dopamine. Glutamate concentrations in the left lateral PFC and left striatum were assessed using 3-Tesla proton magnetic resonance spectroscopy. Striatal presynaptic dopamine synthesis capacity was measured by fluorine-18-l-dihydroxyphenylalanine (F-18-FDOPA) positron emission tomography. First, a negative relationship was observed between glutamate concentrations in lateral PFC and VS dopamine synthesis capacity (n = 28). Second, a positive relationship was revealed between striatal glutamate and VS dopamine synthesis capacity (n = 26). Additionally, the intraindividual difference between PFC and striatal glutamate concentrations correlated negatively with VS dopamine synthesis capacity (n = 24). The present results indicate an involvement of a balance in PFC and striatal glutamate in the regulation of VS dopamine synthesis capacity. This notion points toward a potential mechanism how VS presynaptic dopamine levels are kept in a fine-tuned range. A disruption of this mechanism may account for alterations in striatal dopamine turnover as observed in mental diseases (e.g., in schizophrenia).

SIGNIFICANCE STATEMENT

The present work demonstrates complementary relationships between prefrontal and striatal glutamate and ventral striatal presynaptic dopamine using human imaging measures: a negative correlation between prefrontal glutamate and presynaptic dopamine and a positive relationship between striatal glutamate and presynaptic dopamine are revealed. The results may reflect a regulatory role of prefrontal and striatal glutamate for ventral striatal presynaptic dopamine levels. Such glutamate-dopamine relationships improve our understanding of neurochemical interactions in prefrontostriatal circuits and have implications for the neurobiology of mental disease.