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There has been a growing interest among psychologists, psychiatrists and neuroscientists in applying computational modeling to behavioral data to understand animal and human behavior. Such approaches can be daunting for those without experience. This paper presents a step-by-step tutorial to conduct parameter estimation in R via three techniques: Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP) and Expectation-Maximization with Laplace approximation (EML). We first demonstrate how to simulate a classic reinforcement learning paradigm -- the two-armed bandit task -- for N = 100 subjects; and then explain how to develop the computational model and implement the MLE, MAP and EML methods to recover the parameters. By presenting a sufficiently detailed walkthrough on a familiar behavioral task, we hope this tutorial could benefit readers interested in applying parameter estimation methods in their own research.

BACKGROUND: Exposure to adversity, including unpredictable environments, during early life is associated with neuropsychiatric illness in adulthood. One common factor in this sequela is anhedonia, the loss of responsivity to previously reinforcing stimuli. To accelerate the development of new treatment strategies for anhedonic disorders induced by early-life adversity, animal models have been developed to capture critical features of early-life stress and the behavioral deficits that such stressors induce. We have previously shown that rats exposed to the limited bedding and nesting protocol exhibited blunted reward responsivity in the probabilistic reward task, a touchscreen-based task reverse translated from human studies. METHODS: To test the quantitative limits of this translational platform, we examined the ability of Bayesian computational modeling and probability analyses identical to those optimized in previous human studies to quantify the putative mechanisms that underlie these deficits with precision. Specifically, 2 parameters that have been shown to independently contribute to probabilistic reward task outcomes in patient populations, reward sensitivity and learning rate, were extracted, as were trial-by-trial probability analyses of choices as a function of the preceding trial. RESULTS: Significant deficits in reward sensitivity, but not learning rate, contributed to the anhedonic phenotypes in rats exposed to early-life adversity. CONCLUSIONS: The current findings confirm and extend the translational value of these rodent models by verifying the effectiveness of computational modeling in distinguishing independent features of reward sensitivity and learning rate that complement the probabilistic reward tasks signal detection end points. Together, these metrics serve to objectively quantify reinforcement learning deficits associated with anhedonic phenotypes.

Anhedonia remains a major clinical issue for which there is few effective interventions. Untreated or poorly controlled anhedonia has been linked to worse disease course and increased suicidal behavior across disorders. Taking a proof-of-mechanism approach under the auspices of the National Institute of Mental Health FAST-FAIL initiative, we were the first to show that, in a transdiagnostic sample screened for elevated self-reported anhedonia, 8 weeks of treatment with a kappa-opioid receptor (KOR) antagonist resulted in significantly higher reward-related activation in one of the core hubs of the brain reward system (the ventral striatum), better reward learning in the Probabilistic Reward Task (PRT), and lower anhedonic symptoms, relative to 8 weeks of placebo. Here, we performed secondary analyses of the PRT data to investigate the putative effects of KOR antagonism on anhedonic behavior with more precision by using trial-level model-based Bayesian computational modeling and probability analyses. We found that, relative to placebo, KOR antagonism resulted in significantly higher learning rate (i.e., ability to learn from reward feedback) and a more sustained preference toward the more frequently rewarded stimulus, but unaltered reward sensitivity (i.e., the hedonic response to reward feedback). Collectively, these findings provide novel evidence that in a transdiagnostic sample characterized by elevated anhedonia, KOR antagonism improved the ability to modulate behavior as a function of prior rewards. Together with confirmation of target engagement in the primary report (Krystal et al., Nat Med, 2020), the current findings suggest that further transdiagnostic investigation of KOR antagonism for anhedonia is warranted.

BackgroundAlterations in reward processing are a central feature of depression and may be influenced by inflammation. Indeed, inflammation is associated with deficits in reward-related processes in animal models and with dysregulation in reward-related neural circuitry in humans. However, the downstream behavioral manifestations of such impairments are rarely examined in humans.MethodsThe influenza vaccination was used to elicit a mild inflammatory response in 41 healthy young adults (age range: 18-22, 30 female). Participants provided blood samples and completed behavioral measures of three key aspects of reward-reward motivation, reward learning, and reward sensitivity-before and 1 day after receiving the influenza vaccine.ResultsThe influenza vaccine led to mild but significant increases in circulating levels of the pro-inflammatory cytokine interleukin-6 (IL-6) (p  .288).ConclusionsThese findings contribute to an emerging literature on the nuanced associations between inflammation and reward and demonstrate that even mild alterations in inflammation are associated with multiple facets of reward processing.

Background: The association between major depressive disorder and motivation to invest cognitive effort for rewards is unclear. One reason might be that prior tasks of cognitive effort-based decision-making are limited by potential confounds such as physical effort and temporal delay discounting. Methods: To address these interpretive challenges, we developed a new task – the Cognitive Effort Motivation Task – to assess one's willingness to exert cognitive effort for rewards. Cognitive effort was manipulated by varying the number of items (1, 2, 3, 4, 5) kept in spatial working memory. Twenty-six depressed patients and 44 healthy controls went through an extensive learning session where they experienced each possible effort level 10 times. They were then asked to make a series of choices between performing a fixed low-effort-low-reward or variable higher-effort-higher-reward option during the task. Results: Both groups found the task more cognitively (but not physically) effortful when effort level increased, but they still achieved ⩾80% accuracy on each effort level during training and >95% overall accuracy during the actual task. Computational modelling revealed that a parabolic model best accounted for subjects' data, indicating that higher-effort levels had a greater impact on devaluing rewards than lower levels. These procedures also revealed that MDD patients discounted rewards more steeply by effort and were less willing to exert cognitive effort for rewards compared to healthy participants. Conclusions: These findings provide empirical evidence to show, without confounds of other variables, that depressed patients have impaired cognitive effort motivation compared to the general population. [ABSTRACT FROM AUTHOR]

Background: Treatment for major depressive disorder (MDD) is imprecise and often involves trial-and-error to determine the most effective approach. To facilitate optimal treatment selection and inform timely adjustment, the current study investigated whether neurocognitive variables could predict an antidepressant response in a treatment-specific manner. Methods: In the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial, outpatients with non-psychotic recurrent MDD were first randomized to an 8-week course of sertraline selective serotonin reuptake inhibitor or placebo. Behavioral measures of reward responsiveness, cognitive control, verbal fluency, psychomotor, and cognitive processing speeds were collected at baseline and week 1. Treatment responders then continued on another 8-week course of the same medication, whereas non-responders to sertraline or placebo were crossed-over under double-blinded conditions to bupropion noradrenaline/dopamine reuptake inhibitor or sertraline, respectively. Hamilton Rating for Depression scores were also assessed at baseline, weeks 8, and 16. Results: Greater improvements in psychomotor and cognitive processing speeds within the first week, as well as better pretreatment performance in these domains, were specifically associated with higher likelihood of response to placebo. Moreover, better reward responsiveness, poorer cognitive control and greater verbal fluency were associated with greater likelihood of response to bupropion in patients who previously failed to respond to sertraline. Conclusion: These exploratory results warrant further scrutiny, but demonstrate that quick and non-invasive behavioral tests may have substantial clinical value in predicting antidepressant treatment response. [ABSTRACT FROM AUTHOR]

Apathy is a common, disabling neuropsychiatric syndrome that occurs across many brain disorders and may be associated with diminished motivation in behavioural, cognitive, emotional and social domains. Assessment is complicated by the variability of symptoms across apathy domains and self‐report from patients, which can be misleading due to their lack of insight. Independent evaluation by clinicians also has limitations though if it has to be performed with limited time. Caregiver reports are a viable alternative, but current assessments for them either do not distinguish between different apathy domains or are interview‐based and take long to administer. In this study, we developed a brief caregiver questionnaire version of the recently developed Apathy Motivation Index (AMI), which is a self‐report tool. We confirmed three apathy factors in this new caregiver measure (AMI‐CG) that were also present in the AMI: Behavioural Activation, Emotional Sensitivity and Social Motivation. Furthermore, we validated the scores against more extensive caregiver interviews using the established Lillle apathy rating scale as well as patient self‐reports of apathy, measures of depression, anhedonia, cognition, activities of daily living and caregiver burden across four different neurological conditions: Parkinson's disease, Alzheimer's disease, subjective cognitive impairment and limbic encephalitis. The AMI‐CG showed good internal reliability, external validity and diagnostic accuracy. It also uncovered cases of social apathy overlooked by traditional instruments. Crucially, patients who under‐rated their apathy compared to informants were more likely to have difficulties performing everyday activities and to be a greater burden to caregivers. The findings provide evidence for a multidimensional conceptualization of apathy and an instrument for efficient detection of apathy based on caregiver reports for use in clinical practice. [ABSTRACT FROM AUTHOR]