OBJECTIVE To the authors’ knowledge, no data have been reported on dopamine fluctuations on subsecond timescales in humans with alcohol use disorder (AUD). In this study, dopamine release was monitored in 2 patients with and 2 without a history of AUD during a “sure bet or gamble” (SBORG) decision-making task to begin to characterize how subsecond dopamine responses to counterfactual information, related to psychological notions of regret and relief, in AUD may be altered. METHODS Measurements of extracellular dopamine levels were made once every 100 msec using human voltammetric methods. Measurements were made in the caudate during deep brain stimulation electrode implantation surgeries (for treatment of movement disorders) in patients who did (AUD, n = 2) or did not (non-AUD, n = 2) have a history of AUD. Participants performed an SBORG decision-making task in which they made choices between sure bets and 50%-chance monetary gamble outcomes. RESULTS Fast changes were found in dopamine levels that appear to be modulated by “what could have been” and by patients’ AUD status. Positive counterfactual prediction errors (related to relief) differentiated patients with versus without a history of AUD. CONCLUSIONS Dopaminergic encoding of counterfactual information appears to differ between patients with and without AUD. The current study has a major limitation of a limited sample size, but these data provide a rare insight into dopaminergic physiology during real-time decision-making in humans with an addiction disorder. The authors hope future work will expand the sample size and determine the generalizability of the current results.

Dopaminergic signaling in the striatum has been shown to play a critical role in the perception of time. Decreasing striatal dopamine efficacy is at the core of Parkinson’s disease (PD) motor symptoms and changes in dopaminergic action have been associated with many comorbid non-motor symptoms in PD. We hypothesize that patients with PD perceive time differently and in accordance with their specific comorbid non-motor symptoms and clinical state. We recruited patients with PD and compared individual differences in patients’ clinical features with their ability to judge millisecond to second intervals of time (500ms-1100ms) while on or off their prescribed dopaminergic medications. We show that individual differences in comorbid non-motor symptoms, PD duration, and prescribed dopaminergic pharmacotherapeutics account for individual differences in time perception performance. We report that comorbid impulse control disorder is associated with temporal overestimation; depression is associated with decreased temporal accuracy; and PD disease duration and prescribed levodopa monotherapy are associated with reduced temporal precision and accuracy. Observed differences in time perception are consistent with hypothesized dopaminergic mechanisms thought to underlie the respective motor and non-motor symptoms in PD, but also raise questions about specific dopaminergic mechanisms. In future work, time perception tasks like the one used here, may provide translational or reverse translational utility in investigations aimed at disentangling neural and cognitive systems underlying PD symptom etiology.One Sentence SummaryQuantitative characterization of time perception behavior reflects individual differences in Parkinson’s disease motor and non-motor symptom clinical presentation that are consistent with hypothesized neural and cognitive mechanisms.

In the mammalian brain, midbrain dopamine neuron activity is hypothesized to encode reward prediction errors that promote learning and guide behavior by causing rapid changes in dopamine levels in target brain regions. This hypothesis (and alternatives regarding dopamine's role in punishment-learning) has limited direct evidence in humans. We report intracranial, subsecond measurements of dopamine release in human striatum measured while volunteers (i.e., patients undergoing deep brain stimulation (DBS) surgery) performed a probabilistic reward- and punishment-learning choice task designed to test whether dopamine release encodes only reward prediction errors or whether dopamine release may also encode adaptive punishment-learning signals. Results demonstrate that extracellular dopamine levels can encode both reward and punishment prediction errors, but may do so via independent valence-specific pathways in the human brain.

In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.