Computational psychiatry of impulsivity and risk: how risk and time preferences interact in health and disease

Spontaneous neural activity in the three principal networks underlying delay discounting: a resting-state fMRI study

Delay discounting, the decline in the subjective value of future rewards over time, has traditionally been understood through a tripartite neural network model, comprising the valuation, cognitive control, and prospection networks. To investigate the applicability of this model in a resting-state context, we employed a monetary choice questionnaire to quantify delay discounting and utilized resting-state functional magnetic resonance imaging (rs-fMRI) to explore the role of spontaneous brain activity, specifically regional homogeneity (ReHo), in influencing individual differences in delay discounting across a large cohort (N = 257). Preliminary analyses revealed a significant negative correlation between delay discounting tendencies and the ReHo in both the left insula and the right hippocampus, respectively. Subsequent resting-state functional connectivity (RSFC) analyses, using these regions as seed ROIs, disclosed that all implicated brain regions conform to the three principal networks traditionally associated with delay discounting. Our findings offer novel insights into the role of spontaneous neural activity in shaping individual variations in delay discounting at both regional and network levels, providing the first empirical evidence supporting the applicability of the tripartite network model in a resting-state context.

Metabolic and functional substrates of impulsive decision-making in individuals with heroin addiction after prolonged methadone maintenance treatment

Elevated impulsivity has been frequently reported in individuals with opioid addiction receiving methadone maintenance therapy (MMT), but the underlying neural mechanisms and cognitive subprocesses are not fully understood. We acquired functional magnetic resonance imaging (fMRI) data from 37 subjects with heroin addiction receiving long-term MMT and 33 healthy controls who performed a probabilistic reversal learning task, and measured their resting-state brain glucose using fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG PET). Subjects receiving MMT exhibited significantly elevated self-reported impulsivity, and computational modeling revealed a marked impulsive decision bias manifested as switching more frequently without available evidence. Moreover, this impulsive decision bias was associated with the dose and duration of methadone use, irrelevant to the duration of heroin use. During the task, the switch-related hypoactivation in the left rostral middle frontal gyrus was correlated with the impulsive decision bias while the function of reward sensitivity was intact in subjects receiving MMT. Using prior brain-wide receptor density data, we found that the highest variance of regional metabolic abnormalities was explained by the spatial distribution of μ-opioid receptors among 10 types of neurotransmitter receptors. Heightened impulsivity in individuals receiving prolonged MMT is manifested as atypical choice bias and noise in decision-making processes, which is further driven by deficits in top-down cognitive control, other than reward sensitivity. Our findings uncover multifaceted mechanisms underlying elevated impulsivity in subjects receiving MMT, which might provide insights for developing complementary therapies to improve retention during MMT.

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.

Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control - the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering - adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies.

METHODS

This cross-sectional and correlational study recruited 650 adults (330 males) aged 18-69 years (M = 33.06; MD = 31.00; SD = 10.50), with previously diagnosed ADHD (n = 329) and those from the general community without a history of ADHD (n = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online.

RESULTS

Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor.

CONCLUSIONS

Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.

RISK aversion in Italian forensic and non-forensic patients with schizophrenia spectrum disorders

BACKGROUND

Goal-directed decision-making is a central component of the broader reward and motivation system, and requires the ability to dynamically integrate both positive and negative feedback from the environment in order to maximize rewards and minimize losses over time. Altered decision-making processes, in which individuals fail to consider the negative consequences of their decisions on both themselves and others, may play a role in driving antisocial behaviour.

AIM

The main study aim was to investigate possible differences in loss and risk aversion across matched patients, all with a schizophrenia spectrum disorder (SSD), but who varied according to whether they had a history of serious interpersonal violence or not, and a sample of healthy controls with no history of violence.

RESULTS

The sample included 14 forensic and 21 non-forensic patients with SSD, and 41 healthy controls. Among the three decision-making variables under investigation, risk aversion was the only significant predictor of membership of the three groups, with greater risk aversion among non-forensic patients with SSD compared to healthy controls. No differences were observed across groups in loss aversion and choice consistency.

CONCLUSIONS

This evidence suggests a new potential treatment target for rehabilitative measures aimed at achieving functional improvements in patients with SSD by selectively leveraging the neuro-cognitive processing of reward.

Distinct developmental trajectories for risky and impulsive decision-making in chimpanzees

Human adolescence is characterized by a suite of changes in decision-making and emotional regulation that promote risky and impulsive behavior. Accumulating evidence suggests that behavioral and physiological shifts seen in human adolescence are shared by some primates, yet it is unclear if the same cognitive mechanisms are recruited. We examined developmental changes in risky choice, intertemporal choice, and emotional responses to decision outcomes in chimpanzees, our closest-living relatives. We found that adolescent chimpanzees were more risk-seeking than adults, as in humans. However, chimpanzees showed no developmental change in intertemporal choice, unlike humans, although younger chimpanzees did exhibit elevated emotional reactivity to waiting compared to adults. Comparisons of cortisol and testosterone indicated robust age-related variation in these biomarkers, and patterns of individual differences in choices, emotional reactivity, and hormones also supported a developmental dissociation between risk and choice impulsivity. These results show that some but not all core features of human adolescent decision-making are shared with chimpanzees. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.

A Systematic Review on Common and Distinct Neural Correlates of Risk-taking in Substance-related and Non-substance Related Addictions

Both substance-related as well as non-substance-related addictions may include recurrent engagement in risky actions despite adverse outcomes. We here apply a unified approach and review task-based neuroimaging studies on substance-related (SRAs) and non-substance related addictions (NSRAs) to examine commonalities and differences in neural correlates of risk-taking in these two addiction types. To this end, we conducted a systematic review adhering to the PRISMA guidelines. Two databases were searched with predefined search terms to identify neuroimaging studies on risk-taking tasks in individuals with addiction disorders. In total, 19 studies on SRAs (comprising a total of 648 individuals with SRAs) and 10 studies on NSRAs (comprising a total of 187 individuals with NSRAs) were included. Risk-related brain activation in SRAs and NSRAs was summarized individually and subsequently compared to each other. Results suggest convergent altered risk-related neural processes, including hyperactivity in the OFC and the striatum. As characteristic for both addiction types, these brain regions may represent an underlying mechanism of suboptimal decision-making. In contrast, decreased DLPFC activity may be specific to SRAs and decreased IFG activity could only be identified for NSRAs. The precuneus and posterior cingulate show elevated activity in SRAs, while findings regarding these areas were mixed in NSRAs. Additional scarce evidence suggests decreased ventral ACC activity and increased dorsal ACC activity in both addiction types. Associations between identified activation patterns with drug use severity underpin the clinical relevance of these findings. However, this exploratory evidence should be interpreted with caution and should be regarded as preliminary. Future research is needed to evaluate the findings gathered by this review.

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.

Impulsivity is related to overhasty risk learning in attention-deficit/hyperactivity disorder: A computational psychiatric approach

Attention-deficit/hyperactivity disorder (ADHD) is often accompanied by excessive risky behavior, and an impulsive trait has been proposed to be associated with risk-taking. However, the aspect of the cognitive process that impulsivity influences is not well understood. We hypothesized that impulsivity could be related to an overhasty shifting of beliefs during risk learning, thereby resulting in enhanced risk-taking behavior. In this study, we tested our hypothesis using the Bayesian modeling approach and predicted overhasty learning by a data-driven approach. We used an openly available task-based functional magnetic resonance imaging (fMRI) dataset. Participants with adult ADHD (n = 42) completed the balloon analog risk task (BART). By fitting our computational model that encapsulates the degree of overhasty learning, we estimated the degree of learning bias and investigated its relationship with Barratt Impulsiveness Scale (BIS) outcomes. Moreover, we created a connectome-based predictive model (CPM) based on fMRI data to predict the degree of risk-learning bias. The degree of overhasty learning in ADHD patients was significantly correlated with the BIS score (r = 0.424, p = 0.009). The CPM predicted the 'learning bias' parameter using negatively correlated edges (r = 0.341, p = 0.041; q² = 0.092). The 'hub nodes' in the predictive network were in the frontal lobe, including the orbitofrontal area. Our findings suggest that impulsivity in ADHD patients is associated with overhasty updating of beliefs during risk learning. Weak functional connectivity to the both dorso-lateral prefrontal and orbitofrontal lobes is predictive of the degree of overhasty learning.

Individual Differences in Intertemporal Choice

Intertemporal choice involves deciding between smaller, sooner and larger, later rewards. People tend to prefer smaller rewards that are available earlier to larger rewards available later, a phenomenon referred to as temporal or delay discounting. Despite its ubiquity in human and non-human animals, temporal discounting is subject to considerable individual differences. Here, we provide a critical narrative review of this literature and make suggestions for future work. We conclude that temporal discounting is associated with key socio-economic and health-related variables. Regarding personality, large-scale studies have found steeper temporal discounting to be associated with higher levels of self-reported impulsivity and extraversion; however, effect sizes are small. Temporal discounting correlates negatively with future-oriented cognitive styles and inhibitory control, again with small effect sizes. There are consistent associations between steeper temporal discounting and lower intelligence, with effect sizes exceeding those of personality or cognitive variables, although socio-demographic moderator variables may play a role. Neuroimaging evidence of brain structural and functional correlates is not yet consistent, neither with regard to areas nor directions of effects. Finally, following early candidate gene studies, recent Genome Wide Association Study (GWAS) approaches have revealed the molecular genetic architecture of temporal discounting to be more complex than initially thought. Overall, the study of individual differences in temporal discounting is a maturing field that has produced some replicable findings. Effect sizes are small-to-medium, necessitating future hypothesis-driven work that prioritizes large samples with adequate power calculations. More research is also needed regarding the neural origins of individual differences in temporal discounting as well as the mediating neural mechanisms of associations of temporal discounting with personality and cognitive variables.

Shifting uncertainty intolerance: methylphenidate and attention-deficit hyperactivity disorder

Risk evaluation is a critical component of decision making. Risk tolerance is relevant in both daily decisions and pathological disorders such as attention-deficit hyperactivity disorder (ADHD), where impulsivity is a cardinal symptom. Methylphenidate, a commonly prescribed drug in ADHD, improves attention but has mixed reports on risk-based decision making. Using a double-blinded placebo protocol, we studied the risk attitudes of ADHD patients and age-matched healthy volunteers while performing the 2-step sequential learning task and examined the effect of methylphenidate on their choices. We then applied a novel computational analysis using the hierarchical drift-diffusion model to extract parameters such as threshold ('a'-amount of evidence accumulated before making a decision), drift rate ('v'-information processing speed) and response bias ('z' apriori bias towards a specific choice) focusing specifically on risky choice preference. Critically, we show that ADHD patients on placebo have an apriori bias towards risky choices compared to controls. Furthermore, methylphenidate enhanced preference towards risky choices (higher apriori bias) in both groups but had a significantly greater effect in the patient population independent of clinical scores. Thus, methylphenidate appears to shift tolerance towards risky uncertain choices possibly mediated by prefrontal dopaminergic and noradrenergic modulation. We emphasise the utility of computational models in detecting underlying processes. Our findings have implications for subtle yet differential effects of methylphenidate on ADHD compared to healthy population.

Associations Between Delay Discounting and Connectivity of the Valuation-control System in Healthy Young Adults

The process of valuation assists in determining if an object or course of action is rewarding. Delay discounting is the observed decay of a rewards' subjective value over time. Encoding the subjective value of rewards across a spectrum has been attributed to brain regions belonging to the valuation and executive control systems. The valuation system (VS) encodes reward value over short and long delays, influencing reinforcement learning and reward representation. The executive control system (ECS) becomes more active as choice difficulty increases, integrating contextual and mnemonic information with salience signals in the modulation of decision-making. Here, we aimed to identify resting-state functional connectivity-based patterns of the VS and ECS correlated with value-setting and delay discounting (outside-scanner paradigm) in a large (n = 992) cohort of healthy young adults from the Human Connectome Project (HCP). Results suggest the VS may be involved in value-setting of small, immediate rewards while the ECS may be involved in value-setting and delay discounting for large and small rewards over a range of delays. We observed magnitude sensitive connections involving the posterior cingulate cortex, time-sensitive connections with the ventromedial and lateral prefrontal cortex while connections involving the posterior parietal cortex appeared both magnitude- and time-sensitive. The ventromedial prefrontal cortex and posterior parietal cortex could act as "comparator" regions, weighing the value of small rewards against large rewards across various delay duration to aid in decision-making.

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.

Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications

Our willingness to take risks, our ability to wait or the speed with which to make decisions are central features of our personality. However, it is now recognized that impulsive and risk-taking behaviours are not a unitary construct, and different aspects can be both psychologically and neurally dissociated. The range of neurochemicals and brain systems that govern these behaviours is extensive, and this may be a contributing factor to the phenotypic range seen in the human population. However, this variety can also be pathological as extremes in risk-taking and impulsive behaviours are characteristics of many neuropsychiatric and indeed neurodegenerative disorders. This spans obsessive-compulsive disorder, where behaviour becomes ridged and non-spontaneous, to the nonsensical risk-taking seen in gambling and drug taking. This article is part of the theme issue 'Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications'.