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

Quantitative vs. Qualitative Outcomes: A Longitudinal Study of Risk and Ambiguity in Monetary and Medical Decision-Making

How do decision-makers choose between alternatives offering outcomes that are not easily quantifiable? Previous literature on decisions under uncertainty focused on alternatives with quantifiable outcomes, for example monetary lotteries. In such scenarios, decision-makers make decisions based on success chance, outcome magnitude, and individual preferences for uncertainty. It is not clear, however, how individuals construct subjective values when outcomes are not directly quantifiable. To explore how decision-makers choose when facing non-quantifiable outcomes, we focus here on medical decisions with qualitative outcomes. Specifically, we ask whether decision-makers exhibit the same attitudes towards two types of uncertainty - risk and ambiguity - across domains with quantitative and qualitative outcomes. To answer this question, we designed an online decision-making task where participants made binary choices between alternatives offering either guaranteed lower outcomes or potentially higher outcomes that are associated with some risk and ambiguity. The outcomes of choices were either different magnitudes of monetary gains or levels of improvement in a medical condition. We recruited 429 online participants and repeated the survey in two waves, which allowed us to compare the between-domain attitude consistency with within-domain consistency, over time. We found that risk and ambiguity attitudes were moderately correlated across domains. Over time, risk attitudes had slightly higher correlations compared to across domains, while in ambiguity over-time correlations were slightly weaker. These findings are consistent with the conceptualization of risk attitude as more trait-like, and ambiguity attitudes as more state-like. We discuss the implications and applicability of our novel modeling approach to broader contexts with non-quantifiable outcomes.

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.

How Financial Beliefs and Behaviors Influence the Financial Health of Individuals Struggling with Opioid Use Disorder

The surge in opioid use disorder (OUD) over the past decade escalated opioid overdoses to a leading cause of death in the United States. With adverse effects on cognition, risk-taking, and decision-making, OUD may negatively influence financial well-being. This study examined the financial health of individuals diagnosed with OUD by reviewing financial beliefs and financial behaviors. We evaluated quality of life, perceptions of financial condition during active use and recovery, and total debt. We distributed a 20-item survey to 150 individuals in an outpatient treatment program for OUD in a large metropolitan area, yielding a 56% response rate. The results revealed low overall financial health, with a median debt of USD 12,961 and a quality-of-life score of 72.80, 9.4% lower than the U.S. average (82.10). Most participants (65.75%) reported improved financial health during recovery, while a higher majority (79.45%) worsened during active use. Unemployment affected 42% of respondents, and 9.52% were employed only part-time. Regression analysis highlighted a strong association between lack of full-time employment and a lack of financial advising with total debt. High financial anxiety and active use were associated with lower quality of life. Individuals with OUD may benefit from financial interventions, resources, and counseling to improve their financial health.

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.

Computational markers of risky decision-making predict for relapse to alcohol

BACKGROUND

Relapse remains the major challenge in treatment of alcohol use disorder (AUD). Aberrant decision-making has been found as important cognitive mechanism underlying relapse, but factors associated with relapse vulnerability are unclear. Here, we aim to identify potential computational markers of relapse vulnerability by investigating risky decision-making in individuals with AUD.

METHODS

Forty-six healthy controls and fifty-two individuals with AUD were recruited for this study. The risk-taking propensity of these subjects was investigated using the balloon analog risk task (BART). After completion of clinical treatment, all individuals with AUD were followed up and divided into a non-relapse AUD group and a relapse AUD group according to their drinking status.

RESULTS

The risk-taking propensity differed significantly among healthy controls, the non-relapse AUD group, and the relapse AUD group, and was negatively associated with the duration of abstinence in individuals with AUD. Logistic regression models showed that risk-taking propensity, as measured by the computational model, was a valid predictor of alcohol relapse, and higher risk-taking propensity was associated with greater risk of relapse to drink.

CONCLUSION

Our study presents new insights into risk-taking measurement and identifies computational markers that provide prospective information for relapse to drink in individuals with AUD.

Neural mechanisms of intertemporal and risky decision-making in individuals with internet use disorder: A perspective from directed functional connectivity

Background and aims

The intertemporal and risk decision-making impairments are vital cognitive mechanisms in internet use disorder (IUD). However, the underlying neural mechanisms for these two decision-making dysfunctions in individuals with IUD remain unclear.

Methods

This study employed Functional Near-Infrared Spectroscopy (fNIRS) to record changes in blood oxygen concentration in the prefrontal cortex of individuals with IUD during intertemporal and risk decision-making tasks.

Results

The findings revealed that the intertemporal decision-making deficits in IUD group were primarily associated with reduced activation in the left dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC) and FC from the left dlPFC to the right dlPFC. On the other hand, risk decision-making impairments were linked to decreased OFC activation and weakened functional connectivity from the left dlPFC to the right dlPFC and OFC.

Discussions and Conslusions

These results suggested that while there were common neural mechanisms underlying intertemporal and risk decision-making impairments in individuals with IUD, specific neural foundations existed for each type of dysfunction.

Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder

Self-regulation, the ability to guide behavior according to one's goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test-retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures' construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks.

Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results

BACKGROUND

Substance use disorders (SUDs) represent a major public health risk. Yet, our understanding of the mechanisms that maintain these disorders remains incomplete. In a recent computational modeling study, we found initial evidence that SUDs are associated with slower learning rates from negative outcomes and less value-sensitive choice (low "action precision"), which could help explain continued substance use despite harmful consequences.

METHODS

Here we aimed to replicate and extend these results in a pre-registered study with a new sample of 168 individuals with SUDs and 99 healthy comparisons (HCs). We performed the same computational modeling and group comparisons as in our prior report (doi: 10.1016/j.drugalcdep.2020.108208) to confirm previously observed effects. After completing all pre-registered replication analyses, we then combined the previous and current datasets (N = 468) to assess whether differences were transdiagnostic or driven by specific disorders.

RESULTS

Replicating prior results, SUDs showed slower learning rates for negative outcomes in both Bayesian and frequentist analyses (partial η2=.02). Previously observed differences in action precision were not confirmed. Learning rates for positive outcomes were also similar between groups. Logistic regressions including all computational parameters as predictors in the combined datasets could differentiate several specific disorders from HCs, but could not differentiate most disorders from each other.

CONCLUSIONS

These results provide robust evidence that individuals with SUDs adjust behavior more slowly in the face of negative outcomes than HCs. They also suggest this effect is common across several different SUDs. Future research should examine its neural basis and whether learning rates could represent a new treatment target or moderator of treatment outcome.

Integrating Text Messaging in a Low Threshold Telebuprenorphine Program for New York City Residents with Opioid Use Disorder during COVID-19: A Pilot Randomized Controlled Trial

BACKGROUND

Pragmatic innovations are needed to optimize clinical outcomes among people who use opioids initiating buprenorphine. This pilot randomized controlled trial assessed the feasibility of integrating text messaging in a low threshold telebuprenorphine bridge program for people who use opioids during the COVID-19 pandemic.

METHODS

Eligible adult patients with opioid use disorder inducted on buprenorphine (N = 128) in the NYC Health+Hospitals Virtual Buprenorphine Clinic between May and November 2020 were randomized to an automated texting intervention based on the medical management model versus treatment as usual. A participant feedback survey was administered at 8 weeks (n = 18). Primary outcomes consisted of acceptability (eg, study enrollment, engagement with the intervention) and feasibility (eg, lack of phone number and/or mobile phone ownership) of integrating texting in clinical care. A secondary outcome included retention in treatment at week 8 (ie, active buprenorphine prescription within the prior 7 days).

RESULTS

Nearly all eligible patients consented to enroll in the study (90.8%) and few were excluded because of lack of mobile phone ownership (n = 27, 14.6%). Requests to discontinue receipt of texts (n = 6, 9.4%) was attributed to excessive message frequency, perceived lack of relevancy, and reduced interest in the intervention. Respondents completing the follow-up feedback survey were generally satisfied with the frequency of software-generated messages (14/18, 77.8%) and half shared text content with peers (9/18, 50%). There were no perceived issues with privacy, intrusiveness, or ease of use. Retention did not differ between participants randomized to the texting (M = 5.23 weeks, SD = 3.41) and treatment as usual groups (M = 4.98 weeks, SD = 3.34) at week 8 ( P = 0.676).

CONCLUSIONS

This pilot randomized controlled trial confirms high acceptability and feasibility of integrating an automated texting tool in a telebuprenorphine bridge program. Future studies should assess whether text messaging may be efficacious when combined with staff contact and content addressing social determinants of health.

Cognitive impairment in opioid use disorders: Is there a case for use of nootropics?

Opioid Use Disorders (OUDs) are often associated with cognitive impairments, which may lead to an increased risk of relapse. These cognitive deficits do not resolve with abstinence or medication-assisted treatment and may require targeted management. While psychotherapies and neuromodulation techniques have been studied for their effectiveness, they have certain limitations and challenges. Cognition enhancing prescription drugs like donepezil and memantine, which are used in dementias, have shown promise in a small number of studies examining their role in the reversal of opioid-induced cognitive deficits. The authors explore the potential role of nootropics in improvement of cognitive decline associated with OUDs.

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).

Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results

Background

Substance use disorders (SUDs) represent a major public health risk. Yet, our understanding of the mechanisms that maintain these disorders remains incomplete. In a recent computational modeling study, we found initial evidence that SUDs are associated with slower learning rates from negative outcomes and less value-sensitive choice (low "action precision"), which could help explain continued substance use despite harmful consequences.

Methods

Here we aimed to replicate and extend these results in a pre-registered study with a new sample of 168 individuals with SUDs and 99 healthy comparisons (HCs). We performed the same computational modeling and group comparisons as in our prior report (doi: 10.1016/j.drugalcdep.2020.108208) to confirm previously observed effects. After completing all pre-registered replication analyses, we then combined the previous and current datasets (N = 468) to assess whether differences were transdiagnostic or driven by specific disorders.

Results

Replicating prior results, SUDs showed slower learning rates for negative outcomes in both Bayesian and frequentist analyses (η 2 =.02). Previously observed differences in action precision were not confirmed. Logistic regressions including all computational parameters as predictors in the combined datasets could differentiate several specific disorders from HCs, but could not differentiate most disorders from each other.

Conclusions

These results provide robust evidence that individuals with SUDs have more difficulty adjusting behavior in the face of negative outcomes than HCs. They also suggest this effect is common across several different SUDs. Future research should examine its neural basis and whether learning rates could represent a new treatment target or moderator of treatment outcome.

Neural valuation of rewards and punishments in posttraumatic stress disorder: a computational approach

Posttraumatic stress disorder (PTSD) is associated with changes in fear learning and decision-making, suggesting involvement of the brain's valuation system. Here we investigate the neural mechanisms of subjective valuation of rewards and punishments in combat veterans. In a functional MRI study, male combat veterans with a wide range of posttrauma symptoms (N = 48, Clinician Administered PTSD Scale, CAPS-IV) made a series of choices between sure and uncertain monetary gains and losses. Activity in the ventromedial prefrontal cortex (vmPFC) during valuation of uncertain options was associated with PTSD symptoms, an effect which was consistent for gains and losses, and specifically driven by numbing symptoms. In an exploratory analysis, computational modeling of choice behavior was used to estimate the subjective value of each option. The neural encoding of subjective value varied as a function of symptoms. Most notably, veterans with PTSD exhibited enhanced representations of the saliency of gains and losses in the neural valuation system, especially in ventral striatum. These results suggest a link between the valuation system and the development and maintenance of PTSD, and demonstrate the significance of studying reward and punishment processing within subject.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Development in uncertain contexts: An ecologically informed approach to understanding decision-making during adolescence

Adolescence is a period of development in which youth have new opportunities for decision-making, often in situations where they may have little information or experience to guide their choices. Thus, learning to make decisions under uncertainty is a key challenge during adolescence. To date, researchers have applied economics formalisms to understand the processes that support adolescents in making decisions under two distinct forms of uncertainty: economic risk and economic ambiguity. Economic risk is when the probabilities of outcomes are known. Economic ambiguity is when the probabilities of outcomes are unknown or unknowable. This research has led to foundational knowledge about the basic processes involved in adolescent decision-making, but many experimental paradigms that dissociate economic risk and ambiguity rely on monetary or point-based choices. Given that adolescence is a period of development characterized by a changing social environment, it remains unclear whether the processes that adolescents engage during decision-making on monetary or point-based experimental tasks generalize to their day-to-day experiences in the real world. In this brief piece, we explore how developmental research applying economics formalisms can be bolstered by research on youth's social environments to advance our understanding of decision-making in adolescence. First, we review developmental research by using economic uncertainty paradigms. Next, we highlight research on adolescents' social environments to provide examples of the day-to-day choices that adolescents face among their peers and in their broader communities. Finally, we propose directions for future research integrating these separate approaches to create a more nuanced, ecologically informed understanding of adolescent decision-making.

Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms

Cognitive theories of depression, and mindfulness theories of well-being, converge on the notion that self-judgment plays a critical role in mental health. However, these theories have rarely been tested via tasks and computational modeling analyses that can disentangle the information processes operative in self-judgments. We applied a drift-diffusion computational model to the self-referential encoding task (SRET) collected before and after an 8-week mindfulness intervention (n = 96). A drift-rate regression parameter representing positive-relative to negative-self-referential judgment strength positively related to mindful awareness and inversely related to depression, both at baseline and over time; however, this parameter did not significantly relate to the interaction between mindful awareness and nonjudgmentalness. At the level of individual depression symptoms, at baseline, a spectrum of symptoms (inversely) correlated with the drift-rate regression parameter, suggesting that many distinct depression symptoms relate to valenced self-judgment between subjects. By contrast, over the intervention, changes in only a smaller subset of anhedonia-related depression symptoms showed substantial relationships with this parameter. Both behavioral and model-derived measures showed modest split-half and test-retest correlations. Results support cognitive theories that implicate self-judgment in depression and mindfulness theories, which imply that mindful awareness should lead to more positive self-views.

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.

A Neuroeconomics Approach to Obesity

Obesity is a heterogeneous condition that is affected by physiological, behavioral, and environmental factors. Value-based decision making is a useful framework for integrating these factors at the individual level. The disciplines of behavioral economics and reinforcement learning provide tools for identifying specific cognitive and motivational processes that may contribute to the development and maintenance of obesity. Neuroeconomics complements these disciplines by studying the neural mechanisms underlying these processes. We surveyed recent literature on individual decision characteristics that are most frequently implicated in obesity: discounting the value of future outcomes, attitudes toward uncertainty, and learning from rewards and punishments. Our survey highlighted both consistent and inconsistent behavioral findings. These findings underscore the need to examine multiple processes within individuals to identify unique behavioral profiles associated with obesity. Such individual characterization will inform future studies on the neurobiology of obesity as well as the design of effective interventions that are individually tailored.

A double-blind study assessing the impact of orbitofrontal theta burst stimulation on goal-directed behavior

Patients with disorders of compulsivity show impairments in goal-directed behavior, which have been linked to orbitofrontal cortex (OFC) dysfunction. We recently showed that continuous theta burst stimulation (cTBS), which reduces OFC activity, had a beneficial effect on compulsive behaviors both immediately and at 1 week follow-up compared with inhibitory TBS (iTBS). In this same sample, we investigated whether two behavioral measures of goal-directed control (devaluation success on a habit override task; model-based planning on the two-step task) were also affected by acute modulation of OFC activity. Overall, model-based planning and devaluation success were significantly related to each other and (for devaluation success) to symptoms in our transdiagnostic clinical sample. These measures were moderately to highly stable across time. In individuals with low levels of model-based planning, active cTBS improved devaluation success. Analogous to previously reported clinical effects, this effect was specific to cTBS and not iTBS. Overall, results suggested that measures of goal directed behavior are reliable but less affected by cTBS than clinical self-report. Future research should continue to examine longitudinal changes in behavioral measures to determine their temporal relationship with symptom improvement after treatment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Computational Psychiatry Needs Time and Context

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry.

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.

Reward-Related Decision-Making in Current and Past Disordered Gambling: Implications for Impulsive Choice and Risk Preference in the Maintenance of Gambling Disorder

Impulsive reward-related decision-making (RRDM) is robustly associated with gambling disorder (GD), although its role in the development and perpetuation of GD is still being investigated. This project sought to examine the possible roles of impulsive and risky choice, two aspects of RRDM, in the perpetuation of GD. Additionally, the potential moderating role of comorbid substance misuse was considered. A total of 434 participants with symptoms of current GD and symptoms of concurrent substance use disorder (SUD; n = 105), current GD with past SUD (n = 98), past GD with current SUD (n = 53), or past GD with past substance use disorder (SUD; n = 92), and 96 healthy controls were recruited through MTurk. Participants completed a randomly adjusting delay discounting (a measure of impulsive choice) and probabilistic discounting (a measure of risky choice) task and self-report questionnaires of gambling participation, GD and SUD symptomology, and trait impulsivity. Although control participants showed significantly greater delay discounting compared to individuals with a current or history of GD, no significant group differences emerged between individuals with current GD or a history of GD. Individuals with current GD showed significantly less probabilistic discounting compared to individuals with a history of GD and control participants showed the greatest rates of probabilistic discounting. These effects remained after controlling for lifetime gambling symptom severity and trait impulsivity. Overall, these findings suggest a potential maintaining role of risky choice in gambling disorder, but do not support a maintaining role for impulsive choice.

Dopamine neurons gate the intersection of cocaine use, decision making, and impulsivity

Gambling and substance use disorders are highly comorbid. Both clinical populations are impulsive and exhibit risky decision-making. Drug-associated cues have long been known to facilitate habitual drug-seeking, and the salient audiovisual cues embedded within modern gambling products may likewise encourage problem gambling. The dopamine neurons of the ventral tegmental area (VTA) are exquisitely sensitive to drugs of abuse, uncertain rewards, and reward-paired cues and may therefore be the common neural substrate mediating synergistic features of both disorders. To test this hypothesis, we first gained specific inhibitory control over VTA dopamine neurons by transducing a floxed inhibitory DREADD (AAV5-hSyn-DIO-hM4D(Gi)-mCherry) in rats expressing Cre recombinase in tyrosine hydroxylase neurons. We then trained rats in our cued rat gambling task (crGT), inhibiting dopamine neurons throughout task acquisition and performance, before allowing them to self-administer cocaine in the same diurnal period as crGT sessions. The trajectories of addiction differ in women and men, and the dopamine system may differ functionally across the sexes; therefore, we used male and female rats here. We found that inhibition of VTA dopamine neurons decreased cue-induced risky choice and reduced motor impulsivity in males, but surprisingly, enhanced risky decision making in females. Inhibiting VTA dopamine neurons also prevented cocaine-induced changes in decision making in both sexes, but nevertheless drove all animals to consume more cocaine. These findings show that chronic dampening of dopamine signalling can have both protective and deleterious effects on addiction-relevant behaviours, depending on biological sex and dependent variable of interest.

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks

Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop methods for modeling behavior across multiple tasks by connecting cognitive model parameters to common latent constructs. This approach can be used to assess whether 2 tasks measure the same dimensions of cognition, or actually improve the estimates of cognitive models when there are overlapping cognitive processes between 2 related tasks. The approach is then applied to connecting decision data on 2 behavioral tasks that evaluate clinically relevant deficits, the delay discounting task and Cambridge gambling task, to determine whether they both measure the same dimension of impulsivity. We find that the discounting rate parameters in the models of each task are not closely related, although substance users exhibit more impulsive behavior on both tasks. Instead, temporal discounting on the delay discounting task as quantified by the model is more closely related to externalizing psychopathology like aggression, while temporal discounting on the Cambridge gambling task is related more to response inhibition failures. The methods we develop thus provide a new way to connect behavior across tasks and grant new insights onto the different dimensions of impulsivity and their relation to substance use. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Neural Computations of Threat

A host of learning, memory, and decision-making processes form the individual's response to threat and may be disrupted in anxiety and post-trauma psychopathology. Here we review the neural computations of threat, from the first encounter with a dangerous situation, through learning, storing, and updating cues that predict it, to making decisions about the optimal course of action. The overview highlights the interconnected nature of these processes and their reliance on shared neural and computational mechanisms. We propose an integrative approach to the study of threat-related processes, in which specific computations are studied across the various stages of threat experience rather than in isolation. This approach can generate new insights about the evolution, diagnosis, and treatment of threat-related psychopathology.

Advances in the computational understanding of mental illness

Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.

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.

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.