Learning and generalization from reward and punishment in opioid addiction

Reduced generalization of reward among individuals with subthreshold depression: Behavioral and EEG evidence

Altered stimulus generalization has been well-documented in anxiety disorders; however, there is a paucity of research investigating this phenomenon in the context of depression. Depression is characterized by impaired reward processing and heightened attention to negative stimuli. It is hypothesized that individuals with depression exhibit reduced generalization of reward stimuli and enhanced generalization of loss stimuli. Nevertheless, no study has examined this process and its underlying neural mechanisms. In the present study, we recruited 25 participants with subthreshold depression (SD group) and 24 age-matched healthy controls (HC group). Participants completed an acquisition task, in which they learned to associate three distinct pure tones (conditioned stimuli, CSs) with a reward, a loss, or no outcome. Subsequently, a generalization session was conducted, during which similar tones (generalization stimuli, GSs) were presented, and participants were required to classify them as a reward tone, a loss tone, or neither. The results revealed that the SD group exhibited reduced generalization errors in the early phase of generalization, suggesting a diminished ability to generalize reward-related stimuli. The event-related potential (ERP) results indicated that the SD group exhibited decreased generalization of positive valence to reward-related GSs and heightened generalization of negative valence to loss-related GSs, as reflected by the N1 and P2 components. However, the late positive potential (LPP) was not modulated by depression in reward generalization or loss generalization. These findings suggested that individuals with subthreshold depression may have a blunted or reduced ability to generalize reward stimuli, shedding light on potential treatment strategies targeting this particular process.

Dysfunctional feedback processing in male methamphetamine abusers: Evidence from neurophysiological and computational approaches

Methamphetamine use disorder (MUD) as a major public health risk is associated with dysfunctional neural feedback processing. Although dysfunctional feedback processing in people who are substance dependent has been explored in several behavioral, computational, and electrocortical studies, this mechanism in MUDs requires to be well understood. Furthermore, the current understanding of latent components of their behavior such as learning speed and exploration-exploitation dilemma is still limited. In addition, the association between the latent cognitive components and the related neural mechanisms also needs to be explored. Therefore, in this study, the underlying neurocognitive mechanisms of feedback processing of such impairment, and age/gender-matched healthy controls are evaluated within a probabilistic learning task with rewards and punishments. Mathematical modeling results based on the Q-learning paradigm suggested that MUDs show less sensitivity in distinguishing optimal options. Additionally, it may be worth noting that MUDs exhibited a slight decrease in their ability to learn from negative feedback compared to healthy controls. Also through the lens of underlying neural mechanisms, MUDs showed lower theta power at the medial-frontal areas while responding to negative feedback. However, other EEG measures of reinforcement learning including feedback-related negativity, parietal-P300, and activity flow from the medial frontal to lateral prefrontal regions, remained intact in MUDs. On the other hand, the elimination of the linkage between value sensitivity and medial-frontal theta activity in MUDs was observed. The observed dysfunction could be due to the adverse effects of methamphetamine on the cortico-striatal dopamine circuit, which is reflected in the anterior cingulate cortex activity as the most likely region responsible for efficient behavior adjustment. These findings could help us to pave the way toward tailored therapeutic approaches.

Perceptual Generalization of Alcohol-Related Value Characterizes Risky Drinkers

Generalizing from past experiences to novel situations is critical for adaptive behavior, whereas overgeneralization can promote maladaptive responses (e.g., context-inappropriate fear in anxiety). Here, we propose that overgeneralizing alcohol-related associations characterizes risky drinking. We conducted two online experiments assessing generalization of alcohol-related gains (Study 1) and losses (Study 2) among individuals who engaged in light or risky patterns of drinking (Study 1: N = 88, 24-44 years old; Study 2: N = 87, 21-44 years old). After learning to associate cards with alcohol and non-alcohol-related outcomes, participants chose whether to play with cards varying in perceptual similarity to those shown during conditioning. Finally, participants completed a surprise recognition memory test for all outcomes. Although both groups showed comparable conditioning, we found that risky drinkers overgeneralized alcohol-related gains and losses. Risky drinkers also showed a bias toward recognizing alcohol-related images. These results indicate a novel role for overgeneralization of alcohol-related gains and losses as a mechanism associated with risky drinking.

Stress, associative learning, and decision-making

Exposure to acute and chronic stress has significant effects on the basic mechanisms of associative learning and memory. Stress can both impair and enhance associative learning depending on type, intensity, and persistence of the stressor, the subject's sex, the context that the stress and behavior is experienced in, and the type of associative learning taking place. In some cases, stress can cause or exacerbate the maladaptive behavior that underlies numerous psychiatric conditions including anxiety disorders, obsessive-compulsive disorder, post-traumatic stress disorder, substance use disorder, and others. Therefore, it is critical to understand how the varied effects of stress, which may normally facilitate adaptive behavior, can also become maladaptive and even harmful. In this review, we highlight several findings of associative learning and decision-making processes that are affected by stress in both human and non-human subjects and how they are related to one another. An emerging theme from this work is that stress biases behavior towards less flexible strategies that may reflect a cautious insensitivity to changing contingencies. We consider how this inflexibility has been observed in different associative learning procedures and suggest that a goal for the field should be to clarify how factors such as sex and previous experience influence this inflexibility.

Circuit and Cell-Specific Contributions to Decision Making Involving Risk of Explicit Punishment in Male and Female Rats

Decision making is a complex cognitive process that recruits a distributed network of brain regions, including the basolateral amygdala (BLA) and nucleus accumbens shell (NAcSh). Recent work suggests that communication between these structures, as well as activity of cells expressing dopamine (DA) D2 receptors (D2R) in the NAcSh, are necessary for some forms of decision making; however, the contributions of this circuit and cell population during decision making under risk of punishment are unknown. The current experiments addressed this question using circuit-specific and cell type-specific optogenetic approaches in rats during a decision making task involving risk of punishment. In experiment 1, Long-Evans rats received intra-BLA injections of halorhodopsin or mCherry (control) and in experiment 2, D2-Cre transgenic rats received intra-NAcSh injections of Cre-dependent halorhodopsin or mCherry. Optic fibers were implanted in the NAcSh in both experiments. Following training in the decision making task, BLA→NAcSh or D2R-expressing neurons were optogenetically inhibited during different phases of the decision process. Inhibition of the BLA→NAcSh during deliberation (the time between trial initiation and choice) increased preference for the large, risky reward (increased risk taking). Similarly, inhibition during delivery of the large, punished reward increased risk taking, but only in males. Inhibition of D2R-expressing neurons in the NAcSh during deliberation increased risk taking. In contrast, inhibition of these neurons during delivery of the small, safe reward decreased risk taking. These findings extend our knowledge of the neural dynamics of risk taking, revealing sex-dependent circuit recruitment and dissociable activity of selective cell populations during decision making.SIGNIFICANCE STATEMENT Until recently, the ability to dissect the neural substrates of decision making involving risk of punishment (risk taking) in a circuit-specific and cell-specific manner has been limited by the tools available for use in rats. Here, we leveraged the temporal precision of optogenetics, together with transgenic rats, to probe contributions of a specific circuit and cell population to different phases of risk-based decision making. Our findings reveal basolateral amygdala (BLA)→nucleus accumbens shell (NAcSh) is involved in evaluation of punished rewards in a sex-dependent manner. Further, NAcSh D2 receptor (D2R)-expressing neurons make unique contributions to risk taking that vary across the decision making process. These findings advance our understanding of the neural principles of decision making and provide insight into how risk taking may become compromised in neuropsychiatric diseases.

Circuit and cell-specific contributions to decision making involving risk of explicit punishment in male and female rats

Decision making is a complex cognitive process that recruits a distributed network of brain regions, including the basolateral amygdala (BLA) and nucleus accumbens shell (NAcSh). Recent work suggests that communication between these structures, as well as activity of cells expressing dopamine D2 receptors (D2R) in the NAcSh, are necessary for some forms of decision making; however, the contributions of this circuit and cell population during decision making under risk of punishment are unknown. The current experiments addressed this question using circuit- and cell type-specific optogenetic approaches in rats during a decision-making task involving risk of punishment. In Experiment 1, Long-Evans rats received intra-BLA injections of halorhodopsin or mCherry (control) and in Experiment 2, D2-Cre transgenic rats received intra-NAcSh injections of Cre-dependent halorhodopsin or mCherry. Optic fibers were implanted in the NAcSh in both experiments. Following training in the decision-making task, BLA→NAcSh or D2R-expressing neurons were optogenetically inhibited during different phases of the decision process. Inhibition of the BLA→NAcSh during deliberation (the time between trial initiation and choice) increased choice of the large, risky reward (increased risk taking). Similarly, inhibition during delivery of the large, punished reward increased risk taking, but only in males. Inhibition of D2R-expressing neurons in the NAcSh during deliberation increased risk taking. In contrast, inhibition of these neurons during delivery of the small, safe reward decreased risk taking. These findings extend our knowledge of the neural dynamics of risk taking, revealing sex-dependent circuit recruitment and dissociable activity of selective cell populations during decision making.

Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders

In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.

Slower Learning Rates from Negative Outcomes in Substance Use Disorder over a 1-Year Period and Their Potential Predictive Utility

Computational modelling is a promising approach to parse dysfunctional cognitive processes in substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. We assessed 1-year follow-up data on a sample of treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) that were previously assessed at baseline within a prior computational modelling study. Relative to healthy controls (HCs; N = 48), these participants were found at baseline to show altered learning rates and less precise action selection while completing an explore-exploit decision-making task. Here we replicated these analyses when these individuals returned and re-performed the task 1 year later to assess the stability of baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian and frequentist analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); and (b) intra-class correlations (ICCs) between model parameters at baseline and follow-up were significant and ranged from small to moderate (.25 ≤ ICCs ≤ .54). Exploratory analyses also suggested that learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 ≤ rs ≤ .43). These findings suggest that learning dysfunctions are moderately stable during recovery and could correspond to trait-like vulnerability factors. In addition, computational measures at baseline had some predictive value for changes in substance use severity over time and could be clinically informative.

Impaired Learning From Errors and Punishments and Maladaptive Avoidance-General Mechanisms Underlying Self-Regulation Disorders?

Self-regulation (SR) is an important human function that relates to quality of life in multiple domains including mental health. Previous studies have found important correlates of low SR including impulsivity and poor emotional regulation; however, underpinnings of low SR are incompletely understood. Individuals low in SR frequently engage in maladaptive behaviors (substance abuse, procrastination, etc.) despite negative consequences. This phenomenon suggests that impaired learning from errors and punishments may be important mechanisms underlying low SR. Consistently, previous studies observed impaired error processing in a wide spectrum of individuals with low SR and impaired learning from errors and punishments in SR-related disorders. We also note a possible role for poor emotional regulation and refer to concepts suggesting that engaging in maladaptive behaviors may serve as short term emotion regulation strategies aimed at avoiding or alleviating negative affect. We speculate on transdiagnostic factors underlying poor SR. We propose that impaired error processing (possibly related to striatal functioning) may prevent subjects with low SR from learning from errors and punishments and thus learning better SR skills or tendencies. Additionally, impaired coping in emotionally challenging situations, possibly related to prefrontal-cortical functioning, may lead to maladaptive avoidance. Moreover, maladaptive behaviors may be reinforced by the temporary decreases in negative affect and rewarding values of behaviors. Given existing knowledge gaps, we call for more extensive research and describe possible directions and challenges for future studies.

Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma

BACKGROUND

Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood.

METHODS

We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 147) and healthy controls (HCs; N = 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (N = 49) and HCs (N = 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings.

RESULTS

Results indicate that: (a) SUDs show poorer task performance than HCs (p = 0.03, Cohen's d = 0.33), with model estimates revealing less precise action selection mechanisms (p = 0.004, d = 0.43), a lower learning rate from losses (p = 0.02, d = 0.36), and a greater learning rate from gains (p = 0.04, d = 0.31); and (b) groups do not differ significantly in goal-directed information seeking.

CONCLUSIONS

Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision-making during and after treatment.

Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma

BACKGROUND

Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood.

METHODS

We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 147) and healthy controls (HCs; N = 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (N = 49) and HCs (N = 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings.

RESULTS

Results indicate that: (a) SUDs show poorer task performance than HCs (p = 0.03, Cohen's d = 0.33), with model estimates revealing less precise action selection mechanisms (p = 0.004, d = 0.43), a lower learning rate from losses (p = 0.02, d = 0.36), and a greater learning rate from gains (p = 0.04, d = 0.31); and (b) groups do not differ significantly in goal-directed information seeking.

CONCLUSIONS

Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision-making during and after treatment.

Males are more sensitive to reward and less sensitive to loss than females among people with internet gaming disorder: fMRI evidence from a card-guessing task

BACKGROUND

Many studies have found an interesting issue in the Internet gaming disorder (IGD): males are always observed to be the majority. However, there are little research to exploring the differences in the neural mechanisms between males and females in decision-making process among people with IGD. Therefore, explore the reward/loss processing between different gender with IGD could help in understanding the underlying neural mechanism of IGD.

METHODS

Data from functional magnetic resonance imaging (fMRI) were collected from 111 subjects (IGD: 29 males, 25 females; recreational internet game user (RGU): 36 males, 21 females) while they were performing a card-guessing task. We collected and compared their brain features when facing the win and loss conditions in different groups.

RESULTS

For winning conditions, IGD group showed hypoactivity in the lingual gyrus than RGU group, male players showed hyperactivity in the left caudate nucleus, bilateral cingulate gyrus, right middle frontal gyrus (MFG), right precuneus and inferior parietal lobule relative to the females. And significant sex-by-group interactions results showed higher brain activities in the thalamus, parahippocampal gyrus and lower brain activities in Inferior frontal gyrus (IFG) were observed in males with IGD than females. For losing conditions, IGD group showed hypoactivity in the left lingual gyrus, parahippocampal gyrus and right anterior cingulate cortex (ACC) compared to the RGU group, male players showed hyperactive left caudate nucleus and hypoactive right middle occipital gyrus relative to females. And significant sex-by-group interactions results showed that compared to females with IGD, males with IGD showed decreased brain activities in the IFG and lingual gyrus.

CONCLUSIONS

First, there appeared to be no difference in reward processing between the IGD and RGU group, but IGD showed less sensitivity to loss. Secondly, male players showed more sensitivity to rewards and less sensitivity to losses. Last but not least, males and females showed opposite activation patterns in IGD degree and rewards/losses processing. And male IGD subjects are more sensitive to reward and less sensitive to loss than females, which might be the reason for the gender different rates on IGD.

Computational Markers of Risky Decision-making for Identification of Temporal Windows of Vulnerability to Opioid Use in a Real-world Clinical Setting

IMPORTANCE

Opioid addiction is a major public health problem. Despite availability of evidence-based treatments, relapse and dropout are common outcomes. Efforts aimed at identifying reuse risk and gaining more precise understanding of the mechanisms conferring reuse vulnerability are needed.

OBJECTIVE

To use tools from computational psychiatry and decision neuroscience to identify changes in decision-making processes preceding opioid reuse.

DESIGN, SETTING, AND PARTICIPANTS

A cohort of individuals with opioid use disorder were studied longitudinally at a community-based treatment setting for up to 7 months (1-15 sessions per person). At each session, patients completed a risky decision-making task amenable to computational modeling and standard clinical assessments. Time-lagged mixed-effects logistic regression analyses were used to assess the likelihood of opioid use between sessions (t to t + 1; within the subsequent 1-4 weeks) from data acquired at the current session (t). A cohort of control participants completed similar procedures (1-5 sessions per person), serving both as a baseline comparison group and an independent sample in which to assess measurement test-retest reliability. Data were analyzed between January 1, 2018, and September 5, 2019.

MAIN OUTCOMES AND MEASURES

Two individual model-based behavioral markers were derived from the task completed at each session, capturing a participant's current tolerance of known risks and ambiguity (partially unknown risks). Current anxiety, craving, withdrawal, and nonadherence were assessed via interview and clinic records. Opioid use was ascertained from random urine toxicology tests and self-reports.

RESULTS

Seventy patients (mean [SE] age, 44.7 [1.3] years; 12 women and 58 men [82.9% male]) and 55 control participants (mean [SE] age, 42.4 [1.5] years; 13 women and 42 men [76.4% male]) were included. Of the 552 sessions completed with patients (mean [SE], 7.89 [0.59] sessions per person), 252 (45.7%) directly preceded opioid use events (mean [SE], 3.60 [0.44] sessions per person). From the task parameters, only ambiguity tolerance was significantly associated with increased odds of prospective opioid use (adjusted odds ratio, 1.37 [95% CI, 1.07-1.76]), indicating patients were more tolerant specifically of ambiguous risks prior to these use events. The association of ambiguity tolerance with prospective use was independent of established clinical factors (adjusted odds ratio, 1.29 [95% CI, 1.01-1.65]; P = .04), such that a model combining these factors explained more variance in reuse risk. No significant differences in ambiguity tolerance were observed between patients and control participants, who completed 197 sessions (mean [SE], 3.58 [0.21] sessions per person); however, patients were more tolerant of known risks (B = 0.56 [95% CI, 0.05-1.07]).

CONCLUSIONS AND RELEVANCE

Computational approaches can provide mechanistic insights about the cognitive factors underlying opioid reuse vulnerability and may hold promise for clinical use.

From Isolated Emotional Memories to Their Competition During Conflict

Aversive or rewarding experiences are remembered better than those of lesser survival significance. These emotional memories, whether negative or positive, leave traces in the brain which can later be retrieved and strongly influence how we perceive, how we form associations with environmental stimuli and, ultimately, guide our decision-making. In this review aticle, we outline what constitutes an emotional memory by focusing on threat- and reward-related memories and describe how they are formed in the brain during learning and reformed during retrieval. Finally, we discuss how the field is moving from understanding emotional memory brain circuits separately, towards studying how these two opposing brain systems interact to guide choices during conflict. Here, we outline two novel tasks in rodents that model opposing binary choices (approach or avoid) guided by competing emotional memories. The prefrontal cortex (PFC) is a major integration hub of emotional information which is also known to be critical for decision-making. Consequently, brain circuits that involve this brain region may be key for understanding how the retrieval of emotional memories flexibly orchestrates adaptive choice behavior. Because several mental disorders (e.g., drug addiction and depression) are characterized by deficits in decision-making in the face of conflicting emotional memories (maladaptively giving more weight to one memory over the other), the development of choice-based animal models for emotional regulation could give rise to new approaches for the treatment of these disorders in humans.

Human Self-Domestication and the Extended Evolutionary Synthesis of Addiction: How Humans Evolved a Unique Vulnerability

Humans are more vulnerable to addiction in comparison to all other mammals, including nonhuman primates, yet there is a lack of research addressing this. This paper reviews the field of comparative addiction neuroscience, highlighting the significant inter-species variation in the mesocortical dopaminergic and other neuromodulatory systems involved in addiction. Artificial selection gives rise to significant changes in neuroanatomy, neurophysiology and behaviour as shown in certain rodent strains and other domesticated animals. These changes occur over a few generations, relatively short periods of time in evolutionary terms, and demonstrate how dynamic these neuromodulatory systems are in response to the environment. During the course of human evolution, traits crucial to our survival, expansion and domination (traits such as the ability to innovate, adapt to different environments and thrive in a civilization) have been positively selected for, yet also predispose humans to addiction. This is evident in our unique neurochemistry and receptor-drug activation potencies. Examples of these are provided as possible targets for precision medicine.

The Endogenous Opioid System: Role and Dysfunction Caused by Opioid Therapy

The endogenous opioid system is comprised of a wide array of receptors and ligands that are present throughout the central and peripheral nervous system, the gastrointestinal tract, and the immune system. This explains the multitude of physiological functions it is responsible for including analgesia, mood regulation, and modulation of the stress response. It also plays a pivotal role in modulating the brain's reward center with behavioral and social implications on mood disorders and addiction. Exogenous opioid therapy hijacks the endogenous system and alters its functions contributing to an imbalance that is responsible for the pathogenesis of several disease states.

Generalization of appetitive conditioned responses

A stimulus (conditioned stimulus, CS) associated with an appetitive unconditioned stimulus (US) acquires positive properties and elicits appetitive conditioned responses (CR). Such associative learning has been examined extensively in animals with food as the US, and results are used to explain psychopathologies (e.g., substance‐related disorders or obesity). Human studies on appetitive conditioning exist, too, but we still know little about generalization processes. Understanding these processes may explain why stimuli not associated with a drug, for instance, can elicit craving. Forty‐seven hungry participants underwent an appetitive conditioning protocol during which one of two circles with different diameters (CS+) became associated with an appetitive US (chocolate or salty pretzel, according to participants’ preference) but never the other circle (CS−). During generalization, US were delivered twice and the two CS were presented again plus four circles (generalization stimuli, GS) with gradually increasing diameters from CS− to CS+. We found successful appetitive conditioning as reflected in appetitive subjective ratings (positive valence, higher contingency) and physiological responses (startle attenuation and larger skin conductance responses) to CS+ versus CS−, and, importantly, both measures confirmed generalization as indicated by generalization gradients. Small changes in CS‐US contingency during generalization may have weakened generalization processes on the physiological level. Considering that appetitive conditioned responses can be generalized to non‐US‐associated stimuli, a next important step would be to investigate risk factors that mediate overgeneralization.

Numerous studies demonstrated overgeneralization of conditioned fear as a pathological marker for anxiety disorders as well as stress‐related disorders. Appetitive conditioning is crucially involved in mental disorders such as eating or substance‐related and addictive disorders. Thus far, the generalization processes of appetitive conditioned responses remain unexplored. In this study, we demonstrate for the first time a generalization gradient of appetitive conditioned responses. Our study contributes and extends the understanding of generalization processes by explaining, for instance, why craving can be elicited by stimuli that share physical properties with a stimulus signaling the drug.

Learning functions in short-term cocaine users

Objective

This study examined learning functions in short-term cocaine users and control participants.

Method

Seventeen active cocaine users (reporting 3.5 mean years of cocaine use) and seventeen non-cocaine-using controls (with similar reported levels of alcohol and marijuana use) were compared on tasks measuring different aspects of learning.

Results

The cocaine users performed more poorly on the Weather Prediction and List-Learning tasks, as well as supplementary executive and psychomotor function tasks, than controls.

Conclusions

Individuals with a relatively short duration of cocaine use exhibited moderate weaknesses in probabilistic category learning, verbal learning and psychomotor functions, relative to controls. These weaknesses may underpin difficulty in learning from the probabilistic consequences of behavior and hinder the ability to respond to cognitive-behavioral treatments.

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Learning functions in short-term cocaine users have been understudied.

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Short-term cocaine users performed worse than controls on probabilistic category and verbal learning tasks.

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Short-term cocaine users performed similarly to controls on an equivalence learning task.

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Clinicians should be aware that even short-term cocaine use may be associated with cognitive weaknesses.

Forging Neuroimaging Targets for Recovery in Opioid Use Disorder

The United States is in the midst of an opioid epidemic and lacks a range of successful interventions to reduce this public health burden. Many individuals with opioid use disorder (OUD) consume drugs to relieve physical and/or emotional pain, a pattern that may increasingly result in death. The field of addiction research lacks a comprehensive understanding of physiological and neural mechanisms instantiating this cycle of Negative Reinforcement in OUD, resulting in limited interventions that successfully promote abstinence and recovery. Given the urgency of the opioid crisis, the present review highlights faulty brain circuitry and processes associated with OUD within the context of the Three-Stage Model of Addiction (1). This model underscores Negative Reinforcement processes as crucial to the maintenance and exacerbation of chronic substance use together with Binge/Intoxication and Preoccupation/Anticipation processes. This review focuses on cross-sectional as well as longitudinal studies of relapse and treatment outcome that employ magnetic resonance imaging (MRI), functional near-infrared spectroscopy (fNIRs), brain stimulation methods, and/or electroencephalography (EEG) explored in frequency and time domains (the latter measured by event-related potentials, or ERPs). We discuss strengths and limitations of this neuroimaging work with respect to study design and individual differences that may influence interpretation of findings (e.g., opioid use chronicity/recency, comorbid symptoms, and biological sex). Lastly, we translate gaps in the OUD literature, particularly with respect to Negative Reinforcement processes, into future research directions involving operant and classical conditioning involving aversion/stress. Overall, opioid-related stimuli may lessen their hold on frontocingulate mechanisms implicated in Preoccupation/Anticipation as a function of prolonged abstinence and that degree of frontocingulate impairment may predict treatment outcome. In addition, longitudinal studies suggest that brain stimulation/drug treatments and prolonged abstinence can change brain responses during Negative Reinforcement and Preoccupation/Anticipation to reduce salience of drug cues, which may attenuate further craving and relapse. Incorporating this neuroscience-derived knowledge with the Three-Stage Model of Addiction may offer a useful plan for delineating specific neurobiological targets for OUD treatment.

Lack of associations of the opioid receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) with alcohol dependence: review and meta-analysis of retrospective controlled studies

Background

Studies have sought associations of the opioid receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) with alcohol-dependence, but findings are inconsistent. We summarize the information as to associations of rs1799971 (A > G) and the alcohol-dependence.

Methods

Systematically, we reviewed related literatures using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Embase, PubMed, Web of Knowledge, and Chinese National Knowledge Infrastructure (CNKI) databases were searched using select medical subject heading (MeSH) terms to identify all researches focusing on the present topic up to September 2016. Odds ratios (ORs) along with the 95% confidence interval (95% CI) were estimated in allele model, homozygote model, heterozygote model, dominant model and recessive model. Ethnicity-specific subgroup-analysis, sensitivity analysis, heterogeneity description, and publication-bias assessment were also analyzed.

Results

There were 17 studies, including 9613 patients in the present meta-analysis. The ORs in the 5 genetic-models were 1.037 (95% CI: 0.890, 1.210; p = 0.64), 1.074 (95% CI: 0.831, 1.387; p = 0.586), 1.155 (95% CI: 0.935, 1.427; p = 0.181), 1.261 (95% CI: 1.008, 1.578; p = 0.042), 0.968 (95% CI: 0.758, 1.236; p = 0.793), respectively. An association is significant in the dominant model, but there is no statistical significance upon ethnicity-specific subgroup analysis.

Conclusion

The rs1799971 (A > G) is not strongly associated with alcohol-dependence. However, there are study heterogeneities and limited sample sizes.

The extended evolutionary synthesis and addiction: the price we pay for adaptability

Humans are more likely to become addicted and to stay addicted than are other animals. This chapter is a neurobiological and molecular review of addiction and the cooccurring traits and psychiatric disorders from the perspective of the Extended Evolutionary Synthesis (EES). Addiction is an example of pleiotropy as many common haplotypes that are associated with individual differences in vulnerability to substance dependence express a variety of important brain-based phenotypes such as neuroadaptive processes. The neurochemical mechanisms of addiction are shared with behavioral flexibility and the ability to innovate, which are hallmark features of our species. The dopaminergic system provides a link between addiction and the cooccurring traits and psychiatric disorders evident in the shared genetic profile. A hypofunctioning dopaminergic system is also a common characteristic feature of addiction and the cooccurring traits and psychiatric disorders. Epigenetics allows for environmental factors to create lasting and heritable phenotypic changes enabling rapid adaptation to an environment. Addiction "high-jacks" this system as well as the neurochemical mechanisms that control flexibility and innovation and is, thus, the price we pay for adaptability.

Reward and punishment-based compound cue learning and generalization in opiate dependency

Substance dependence is thought to be mediated by abnormalities in cognitive abilities, but how this impacts decision-making remains unclear. This study aimed to test whether people who are opiate dependent differed from never-dependent controls in learning from reward and punishment or in the generalization of learning to novel conditions. Participants with opiate dependency consisted of 21 people who were outpatients in a methadone maintenance program; the control group consisted of 21 healthy participants with no histories of substance abuse. Subjects completed a computer-based task that involved two phases: the training phase involved participants being presented with compound stimulus (a shape and color) in each trial, with the goal of learning which compounds to 'pick' for rewards or 'skip' to avoid punishment. The test phase involved a transfer test, where stimuli from the first phase were combined together to form novel compounds without feedback. The control group demonstrated fewer errors compared to opiate-dependent individuals during the training phase. In the test phase, controls used prior knowledge of both shapes and colors in responding; however, opiate-dependent individuals used shapes but did not use their knowledge of color to modulate responding. When performance during training was equated in the groups using a learning threshold, this difference between groups on the generalization test remained. A deficit in learning generalization might be indicative of group differences in learning strategies in operation during training; however, future work is necessary to uncover the specific neural substrates in action during transfer tasks, and to determine the effects of acute methadone dosage on decision-making.