Using pharmacological manipulations to study the role of dopamine in human reward functioning: A review of studies in healthy adults

Webber, Heather E.; Lopez-Gamundi, Paula; Stamatovich, Sydney N.; Wit, Harriet de; Wardle, Margaret C.

doi:10.1016/j.neubiorev.2020.11.004

Cited by 27 publications

(30 citation statements)

References 252 publications

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“…An investigation in the human VS might be particularly challenging since the animal literature has highlighted the presence of hedonic "coldspots" in the Nucleus Accumbens shell, which are typically activated by the same ligands as the hedonic hotspots (e.g., opioids), but inhibit-rather than amplify-hedonic expressions (Pecina and Berridge, 2005;Castro and Berridge, 2014). More generally, the impact of manipulations of opioid and dopamine manipulations on human reward processing appears to be modulated by several factors, increasing the complexity of these investigations in humans (Webber et al, 2020;Meier et al, 2021). Finally, it is worth noting that we did not determine our sample size by means of a power analysis.…”

Section: Discussionmentioning

confidence: 99%

Differential Contributions of Ventral Striatum Subregions to the Motivational and Hedonic Components of the Affective Processing of Reward

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Differential Contributions of Ventral Striatum Subregions to the Motivational and Hedonic Components of the Affective Processing of Reward

et al. 2022

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“…Dopamine RPE signaling in the dorsal striatum -observed both in the firing patterns of dopamine neurons and in dopamine concentration changes in the striatum -is essential for reinforcement learning [34][35][36]. While this proposal has been directly tested in animals using optogenetics [35], pharmacological manipulations of dopaminergic RPE signaling in the dorsal striatum rigorously in humans has been difficult (e.g., dopamine drugs have differing effects on various brain regions and reward phases [37], and no D1 agonist drugs are available for human use). Motivated by evidence demonstrating that 10-Hz rTMS to the left DLPFC can enhance dopaminergic activity in the dorsal striatum [3], we investigated the impact of rTMS on behavioral and computational measures of striatal-based reinforcement learning in healthy subjects.…”

Section: Discussionmentioning

confidence: 99%

Causal effects of prefrontal transcranial magnetic stimulation on dopamine-mediated reinforcement learning in healthy adults

Biernacki

Myers

Cole³

et al. 2022

Preprint

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Background: 10-Hz repetitive transcranial magnetic stimulation (rTMS) to the left dorsal lateral prefrontal cortex (DLPFC) has been shown to increase dopaminergic activity in the dorsal striatum, a region strongly implicated in reinforcement learning. However, the behavioural influence of this effect remains largely unknown. Objective: Here, we tested the causal effects of rTMS on behavioral and computational characteristics of reinforcement learning. Methods: 40 healthy individuals were randomized into Active and Sham rTMS groups. Each participant underwent one 10-Hz rTMS session (1500 pulses) in which stimulation was applied over the left DLPFC using a robotic arm. Participants then completed a reinforcement learning task sensitive to striatal dopamine functioning. Participants trial-to-trial training choices were modelled using a reinforcement learning model (Q-learning) that calculates separate learning rates associated with positive and negative reward prediction errors. Results: Subjects receiving Active TMS exhibited an increased reward rate (number of correct responses per second of task activity) compared to the Sham rTMS group. Computationally, the Active rTMS group displayed a higher learning rate for correct trials (αG) compared to incorrect trials (αL). Finally, when tested with novel pairs of stimuli, the Active group displayed extremely fast reaction times, and a trend towards a higher reward rate. Conclusions: The present study provided specific behavioral and computational accounts of altered striatal-mediated reinforcement learning induced by a proposed increase of dopamine activity by 10-Hz rTMS to the left DLPFC. Together, these findings bolster the use of TMS to target neurocognitive disturbances attributed to the dysregulation of dopaminergic-striatal circuits.

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“…However, while animal models consistently demonstrate that dopamine signals code RPEs ( Schultz, 2016a ), research examining pharmacological manipulations of dopamine in healthy participants offer mixed findings. While dopamine antagonism has been demonstrated to consistently decrease reward learning, dopamine antagonism and dietary manipulations of dopamine offer mixed results ( Webber et al, 2021 ). These discrepancies could be due to a number of factors including drug manipulations, dosing regimens, or the possibility that there is an optimal level of dopamine for reward-related learning, with increases beyond this level impairing reward-related functioning ( Vaillancourt et al, 2013 ).…”

Section: Antidepressant Drugs and Predictive Processesmentioning

confidence: 99%

A Predictive Coding Framework for Understanding Major Depression

Gilbert

Wusinich

Zarate

2022

Front. Hum. Neurosci.

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Predictive coding models of brain processing propose that top-down cortical signals promote efficient neural signaling by carrying predictions about incoming sensory information. These “priors” serve to constrain bottom-up signal propagation where prediction errors are carried via feedforward mechanisms. Depression, traditionally viewed as a disorder characterized by negative cognitive biases, is associated with disrupted reward prediction error encoding and signaling. Accumulating evidence also suggests that depression is characterized by impaired local and long-range prediction signaling across multiple sensory domains. This review highlights the electrophysiological and neuroimaging evidence for disrupted predictive processing in depression. The discussion is framed around the manner in which disrupted generative predictions about the sensorium could lead to depressive symptomatology, including anhedonia and negative bias. In particular, the review focuses on studies of sensory deviance detection and reward processing, highlighting research evidence for both disrupted generative predictions and prediction error signaling in depression. The role of the monoaminergic and glutamatergic systems in predictive coding processes is also discussed. This review provides a novel framework for understanding depression using predictive coding principles and establishes a foundational roadmap for potential future research.

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Using pharmacological manipulations to study the role of dopamine in human reward functioning: A review of studies in healthy adults

Cited by 27 publications

References 252 publications

Differential Contributions of Ventral Striatum Subregions to the Motivational and Hedonic Components of the Affective Processing of Reward

Differential Contributions of Ventral Striatum Subregions to the Motivational and Hedonic Components of the Affective Processing of Reward

Causal effects of prefrontal transcranial magnetic stimulation on dopamine-mediated reinforcement learning in healthy adults

A Predictive Coding Framework for Understanding Major Depression

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