Major depression symptom severity associations with willingness to exert effort and patch foraging strategy.

Individuals with major depressive disorder (MDD) can experience reduced motivation and cognitive function, leading to challenges with goal-directed behavior. When selecting goals, people maximize 'expected value' by selecting actions that maximize potential reward while minimizing associated costs, including effort 'costs' and the opportunity cost of time. In MDD, differential weighing of costs and benefits are theorized mechanisms underlying changes in goal-directed cognition and may contribute to symptom heterogeneity. We used the Effort Foraging Task to quantify cognitive and physical effort costs, and patch leaving thresholds in low effort conditions (hypothesized to reflect perceived opportunity cost of time) and investigated their shared versus distinct relationships to clinical features in participants with MDD (N=52, 43 in-episode) and comparisons (N=27). Contrary to our predictions, none of the decision-making measures differed with MDD diagnosis. However, each of the measures were related to symptom severity, over and above effects of ability (i.e., performance). Greater anxiety symptoms were selectively associated with lower cognitive effort cost (i.e. greater willingness to exert effort). Anhedonia symptoms were associated with increased physical effort costs. Finally, greater physical anergia was related to decreased patch leaving thresholds. Markers of effort-based decision-making may inform understanding of MDD heterogeneity. Increased willingness to exert cognitive effort may contribute to anxiety symptoms such as rumination and worry. The association of decreased leaving thresholds with symptom severity is consistent with reward rate-based accounts of reduced vigor in MDD. Future research should address subtypes of depression with or without anxiety, which may relate differentially to cognitive effort decisions.

Comparing experience- and description-based economic preferences across 11 countries.

Recent evidence indicates that reward value encoding in humans is highly context dependent, leading to suboptimal decisions in some cases, but whether this computational constraint on valuation is a shared feature of human cognition remains unknown. Here we studied the behaviour of n = 561 individuals from 11 countries of markedly different socioeconomic and cultural makeup. Our findings show that context sensitivity was present in all 11 countries. Suboptimal decisions generated by context manipulation were not explained by risk aversion, as estimated through a separate description-based choice task (that is, lotteries) consisting of matched decision offers. Conversely, risk aversion significantly differed across countries. Overall, our findings suggest that context-dependent reward value encoding is a feature of human cognition that remains consistently present across different countries, as opposed to description-based decision-making, which is more permeable to cultural factors.

The utility of a latent-cause framework for understanding addiction phenomena.

Computational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomena such as craving and relapse. Moreover, they cannot account for goal-directed aspects of addiction that necessitate contrasting, model-based formulations. Here we synthesize a growing body of evidence and propose that a latent-cause framework can help unify our understanding of several recurrent phenomena in addiction, by viewing them as the inferred return of previous, persistent "latent causes". We demonstrate that applying this framework to Pavlovian and instrumental settings can help account for defining features of craving and relapse such as outcome-specificity, generalization, and cyclical dynamics. Finally, we argue that this framework can bridge model-free and model-based formulations, and account for individual variability in phenomenology by accommodating the memories, beliefs, and goals of those living with addiction, motivating a centering of the individual, subjective experience of addiction and recovery.

The role of social connection in opioid use disorder treatment engagement.

OBJECTIVE: Medications for opioid use disorder (OUD or MOUD) treatment combining pharmacotherapy with psychosocial support are effective for managing OUD. However, treatment engagement remains a challenge, with retention rates ∼30%-50%. Although social connection has been identified as important to recovery, it remains unclear whether and how social factors can bolster participation in treatment. METHOD: Individuals receiving MOUD at three outpatient treatment programs (N = 82) and healthy community controls (N = 62) completed validated measures assessing social connection including (a) size, diversity, and embeddedness of social networks; (b) perceived social support and criticism within familial relationships; and (c) subjective social status. For those receiving MOUD, we also examined how aspects of social connection related to opioid (re)use and treatment engagement (medication adherence, group, and individual meeting attendance) assessed over ∼8 weeks/person. RESULTS: Compared to controls, individuals receiving MOUD had smaller and less diverse and embedded social networks (Cohen's d > 0.4), and despite similar levels of perceived social support (d = 0.02), reported higher levels of social criticism (d = 0.6) and lower subjective social status (d = 0.5). Within the MOUD group, higher social network indices correlated specifically with higher therapeutic group attendance (Rs > 0.30), but not medication adherence, while higher levels of perceived criticism correlated with more frequent opioid use (R = 0.23). Results were mostly robust to control for sociodemographic variables, psychological distress/COVID-19, and treatment duration, but differed by MOUD type/program. CONCLUSIONS: These findings highlight the potential importance of assessing an individual's social capital, promoting positive social connection, and continuing to assess the implementation and value of psychosocial support in MOUD treatment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Recent Opioid Use Impedes Range Adaptation in Reinforcement Learning in Human Addiction.

BACKGROUND: Drugs like opioids are potent reinforcers thought to co-opt value-based decisions by overshadowing other rewarding outcomes, but how this happens at a neurocomputational level remains elusive. Range adaptation is a canonical process of fine-tuning representations of value based on reward context. Here, we tested whether recent opioid exposure impacts range adaptation in opioid use disorder, potentially explaining why shifting decision making away from drug taking during this vulnerable period is so difficult. METHODS: Participants who had recently (<90 days) used opioids (n = 34) or who had abstained from opioid use for ≥ 90 days (n = 20) and comparison control participants (n = 44) completed a reinforcement learning task designed to induce robust contextual modulation of value. Two models were used to assess the latent process that participants engaged while making their decisions: 1) a Range model that dynamically tracks context and 2) a standard Absolute model that assumes stationary, objective encoding of value. RESULTS: Control participants and ≥90-days-abstinent participants with opioid use disorder exhibited choice patterns consistent with range-adapted valuation. In contrast, participants with recent opioid use were more prone to learn and encode value on an absolute scale. Computational modeling confirmed the behavior of most control participants and ≥90-days-abstinent participants with opioid use disorder (75%), but a minority in the recent use group (38%), was better fit by the Range model than the Absolute model. Furthermore, the degree to which participants relied on range adaptation correlated with duration of continuous abstinence and subjective craving/withdrawal. CONCLUSIONS: Reduced context adaptation to available rewards could explain difficulty deciding about smaller (typically nondrug) rewards in the aftermath of drug exposure.

Reduced neural encoding of utility prediction errors in cocaine addiction.

Influential accounts of addiction posit alterations in adaptive behavior driven by deficient dopaminergic prediction errors (PEs), signaling the discrepancy between actual and expected reward. Dopamine neurons encode these error signals in subjective terms, calibrated by individual risk preferences, as "utility" PEs. It remains unclear, however, whether people with drug addiction have PE deficits or their computational source. Here, using an analogous task to prior single-unit studies with known expectancies, we show that fMRI-measured PEs similarly reflect utility PEs. Relative to control participants, people with chronic cocaine addiction demonstrate reduced utility PEs in the dopaminoceptive ventral striatum, with similar trends in orbitofrontal cortex. Dissecting this PE signal into its subcomponent terms attributed these reductions to weaker striatal responses to received reward/utility, whereas suppression of activity with reward expectation was unchanged. These findings support that addiction may fundamentally disrupt PE signaling and reveal an underappreciated role for perceived reward value in this mechanism.

From Computation to Clinic.

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

Outcome context-dependence is not WEIRD: Comparing reinforcement- and description-based economic preferences worldwide.

Recent evidence indicates that reward value encoding in humans is highly context-dependent, leading to suboptimal decisions in some cases. But whether this computational constraint on valuation is a shared feature of human cognition remains unknown. To address this question, we studied the behavior of individuals from across 11 countries of markedly different socioeconomic and cultural makeup using an experimental approach that reliably captures context effects in reinforcement learning. Our findings show that all samples presented evidence of similar sensitivity to context. Crucially, suboptimal decisions generated by context manipulation were not explained by risk aversion, as estimated through a separate description-based choice task (i.e., lotteries) consisting of matched decision offers. Conversely, risk aversion significantly differed across countries. Overall, our findings suggest that context-dependent reward value encoding is a hardcoded feature of human cognition, while description-based decision-making is significantly sensitive to cultural factors.

A Proof-of-Concept Ecological Momentary Assessment Study of Day-Level Dynamics in Value-Based Decision-Making in Opioid Addiction.

Background: Drug addiction is thought to be characterized by risky and impulsive behavior despite harmful consequences. Whether these aspects of value-based decision-making in people with addiction are stable and trait-like, and the degree to which they vary within-person and are sensitive to changes in psychological state, remains unknown. In this pilot study, we examined the feasibility of distinguishing these state- vs. trait-like components by probing day-level dynamics of risk and time preferences in patients with opioid use disorder (OUD) as they engaged with their natural environment. Methods: Twenty-three individuals with OUD receiving outpatient treatment (40% female; M = 45.67 [SD = 13.16] years of age) and twenty-one matched healthy community controls (47% female; M = 49.67 [SD = 14.38] years of age) participated in a 28-day smartphone-based ecological momentary assessment study (1085 person days; M = 24.66, SD = 5.84). Random prompts administered daily assessed subjects' psychological state (e.g., mood) and economic preferences for real delayed and risky monetary rewards. Results: Subjects demonstrated dynamic decision-making preferences, with 40-53% of the variation in known risk and ambiguity tolerance, and 67% in discounting, attributable to between-person vs. within-person (day-to-day) differences. We found that changes in psychological state were related to changes in risk preferences, with patients preferring riskier offers on days they reported being in a better mood but no differences between groups in aggregate level behavior. By contrast, temporal discounting was increased overall in patients compared to controls and was unrelated to global mood. The study was well-tolerated, but compliance rates were moderate and lower in patients. Conclusion: Our data support the idea that decision-making preferences in drug addiction exhibit substantial within-person variability and that this variability can be well-captured using remote data collection methods. Preliminary findings suggested that aspects of decision-making related to consideration of risk may be more sensitive to within-person change in global psychological state while those related to consideration of delay to reward, despite also being somewhat variable, stably differ from healthy levels. Identifying the cognitive factors that contribute to opioid use risk in a "real-world" setting may be important for identifying unique, time-sensitive targets for intervention.

A neuroeconomic signature of opioid craving: How fluctuations in craving bias drug-related and nondrug-related value.

How does craving bias decisions to pursue drugs over other valuable, and healthier, alternatives in addiction? To address this question, we measured the in-the-moment economic decisions of people with opioid use disorder as they experienced craving, shortly after receiving their scheduled opioid maintenance medication and ~24 h later. We found that higher cravers had higher drug-related valuation, and that moments of higher craving within-person also led to higher drug-related valuation. When experiencing increased opioid craving, participants were willing to pay more for personalized consumer items and foods more closely related to their drug use, but not for alternative "nondrug-related" but equally desirable options. This selective increase in value with craving was greater when the drug-related options were offered in higher quantities and was separable from the effects of other fluctuating psychological states like negative mood. These findings suggest that craving narrows and focuses economic motivation toward the object of craving by selectively and multiplicatively amplifying perceived value along a "drug relatedness" dimension.

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.

Impulsivity and Medical Care Utilization in Veterans Treated for Substance Use Disorder.

BACKGROUND: Impulsivity has been defined by acting rashly during positive mood states (positive urgency; PU) or negative mood states (negative urgency; NU) and by excessive de-valuation of deferred rewards. These behaviors reflect a "live in the now" mentality that is not only characteristic of many individuals with severe substance use disorder (SUD) but also impedes medical treatment compliance and could result in repeated hospitalizations or other poor health outcomes. Purpose/objectives: We sought preliminary evidence that impulsivity may relate to adverse health outcomes in the veteran population. Impulsivity measured in 90 veterans receiving inpatient or outpatient SUD care at a Veterans Affairs Medical Center was related to histories of inpatient/residential care costs, based on VA Health Economics Resource Center data. Results: We found that positive urgency, lack of persistence and lack of premeditation, but not sensation-seeking or preference for immediate or risky rewards, were significantly higher in veterans with a history of one or more admissions for VA-based inpatient or residential health care that either included (n = 30) or did not include (n = 29) an admission for SUD care. Among veterans with a history of inpatient/residential care for SUD, NU and PU, but not decision-making behavior, correlated with SUD care-related costs. Conclusions/Importance: In veterans receiving SUD care, questionnaire-assessed trait impulsivity (but not decision-making) related to greater care utilization within the VA system. This suggests that veterans with high impulsivity are at greater risk for adverse health outcomes, such that expansion of cognitive interventions to reduce impulsivity may improve their health.

Evidence for Dopamine Abnormalities in the Substantia Nigra in Cocaine Addiction Revealed by Neuromelanin-Sensitive MRI.

OBJECTIVE: Recent evidence supports the use of neuromelanin-sensitive MRI (NM-MRI) as a novel tool to investigate dopamine function in the human brain. The authors investigated the NM-MRI signal in individuals with cocaine use disorder, compared with age- and sex-matched control subjects, based on previous imaging studies showing that this disorder is associated with blunted presynaptic striatal dopamine. METHODS: NM-MRI and T1-weighted images were acquired from 20 participants with cocaine use disorder and 35 control subjects. Diagnostic group effects in NM-MRI signal were determined using a voxelwise analysis within the substantia nigra. A subset of 20 cocaine users and 17 control subjects also underwent functional MRI imaging using the monetary incentive delay task, in order to investigate whether NM-MRI signal was associated with alterations in reward processing. RESULTS: Compared with control subjects, cocaine users showed significantly increased NM-MRI signal in ventrolateral regions of the substantia nigra (area under the receiver operating characteristic curve=0.83). Exploratory analyses did not find a significant correlation of NM-MRI signal to activation of the ventral striatum during anticipation of monetary reward. CONCLUSIONS: Given that previous imaging studies show decreased dopamine signaling in the striatum, the finding of increased NM-MRI signal in the substantia nigra provides additional insight into the pathophysiology of cocaine use disorder. One interpretation is that cocaine use disorder is associated with a redistribution of dopamine between cytosolic and vesicular pools, leading to increased accumulation of neuromelanin. The study findings thus suggest that NM-MRI can serve as a practical imaging tool for interrogating the dopamine system in addiction.

Trait impulsivity and acute stress interact to influence choice and decision speed during multi-stage decision-making.

Impulsivity and stress exposure are two factors that are associated with changes in reward-related behavior in ways that are relevant to both healthy and maladaptive decision-making. Nonetheless, little empirical work has examined the possible independent and joint effects of these factors upon reward learning. Here, we sought to examine how trait impulsivity and acute stress exposure affect participants' choice behavior and decision speed in a two-stage sequential reinforcement-learning task. We found that more impulsive participants were more likely to repeat second-stage choices after previous reward, irrespective of stress condition. Exposure to stress, on the other hand, was associated with an increased tendency to repeat second-stage choices independent of whether these choices previously led to a reward, and this tendency was exacerbated in more impulsive individuals. Such interaction effects between stress and impulsivity were also found on decision speed. Stress and impulsivity levels interacted to drive faster choices overall (again irrespective of reward) at both task stages, while reward received on the previous trial slowed subsequent first-stage choices, particularly among impulsive individuals under stress. Collectively, our results reveal novel, largely interactive effects of trait impulsivity and stress exposure and suggest that stress may reveal individual differences in decision-making tied to impulsivity that are not readily apparent in the absence of stress.

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.