Sub-Second Dopamine Detection in Human Striatum

Kishida, Kenneth T.; Sandberg, Stefan G.; Lohrenz, Terry; Comair, Youssef G.; Sáez, Ignacio; Phillips, Paul E. M.; Montague, P. Read

doi:10.1371/journal.pone.0023291

Cited by 105 publications

(159 citation statements)

References 20 publications

Supporting

Mentioning

158

Contrasting

Order By: Relevance

“…The extended carbon-fiber microsensor is constructed to match the dimensions of the tungsten microelectrodes used for functional mapping during DBS-electrode implantation surgery following ref. 21. Fig.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward

Kishida

Sáez

Lohrenz

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

186

283

View full text Add to dashboard Cite

In the mammalian brain, dopamine is a critical neuromodulator whose actions underlie learning, decision-making, and behavioral control. Degeneration of dopamine neurons causes Parkinson's disease, whereas dysregulation of dopamine signaling is believed to contribute to psychiatric conditions such as schizophrenia, addiction, and depression. Experiments in animal models suggest the hypothesis that dopamine release in human striatum encodes reward prediction errors (RPEs) (the difference between actual and expected outcomes) during ongoing decision-making. Blood oxygen level-dependent (BOLD) imaging experiments in humans support the idea that RPEs are tracked in the striatum; however, BOLD measurements cannot be used to infer the action of any one specific neurotransmitter. We monitored dopamine levels with subsecond temporal resolution in humans (n = 17) with Parkinson's disease while they executed a sequential decision-making task. Participants placed bets and experienced monetary gains or losses. Dopamine fluctuations in the striatum fail to encode RPEs, as anticipated by a large body of work in model organisms. Instead, subsecond dopamine fluctuations encode an integration of RPEs with counterfactual prediction errors, the latter defined by how much better or worse the experienced outcome could have been. How dopamine fluctuations combine the actual and counterfactual is unknown. One possibility is that this process is the normal behavior of reward processing dopamine neurons, which previously had not been tested by experiments in animal models. Alternatively, this superposition of error terms may result from an additional yet-to-be-identified subclass of dopamine neurons.dopamine | reward prediction error | counterfactual prediction error | decision-making | human fast-scan cyclic voltammetry

show abstract

Section: Methodsmentioning

confidence: 99%

“…Our FSCV protocol follows previous work in rodents (21,47,48). An electrochemical conditioning protocol (see Fig.…”

Section: Methodsmentioning

confidence: 99%

Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward

Kishida

Sáez

Lohrenz

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

186

283

View full text Add to dashboard Cite

show abstract

“…The first is the re-260 ward prediction error (RPE), which quantifies the difference between the actual and 261 predicted value of outcomes. The phasic activity [46] of midbrain dopamine neurons 262 and the local concentration of dopamine [26,37] in target regions follow this pattern. 263 The second component is the learning rate, which determines how much change is operating at an appropriate timescale, could boost the effective learning rate.…”

mentioning

confidence: 87%

“…This is broadly evident in the mam-34 malian brain, from the restoration of the critical period for the visual system of rodents 35 occasioned by local infusion of 5-HT [58] to the impairment of particular aspects of 36 associative learning arising from 5-HT depletion in monkeys [7,59]. Despite theoret- 37 ical suggestions for an association with aversive learning [19,53,13,6,15], direct 38 experimental tests into serotonin's role in RL tasks have led to a complex pattern of 39 results [12,52,42,45,22,11]. For instance, recent optogenetic studies reporting that 40 stimulating 5-HT neurons could lead to positive reinforcement [42] do not appear to 41 be consistent with other optogenetic findings, which instead suggest an involvement 42 with patience [22,45] and even locomotion [11].…”

mentioning

confidence: 99%

The Long and the Short of Serotonergic Stimulation: Optogenetic activation of dorsal raphe serotonergic neurons changes the learning rate for rewards

Iigaya

Fonseca

Murakami

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…For this study, Kishida et al (7) recruited patients undergoing surgery to implant deep brain stimulators (DBS) to treat the symptoms of Parkinson's disease. Because DBS surgery requires direct access to the brain, investigators were able to record dopamine levels inside the striatum (caudate and putamen) using a voltammetric probe they previously developed and validated (13).…”

Section: Human Dopamine Recordings Challenge the Reward Prediction Ermentioning

confidence: 99%

Dopamine: Context and counterfactuals

Platt

2015

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Dopamine and Reward Prediction ErrorsHow does the brain learn what feels good? How does it adapt to new foods, bad bets, and played-out songs? How is it tricked into wanting ever-increasing doses of heroin or cocaine? Is there a simple rule that links neural signaling to the fundamental approach and avoidance responses on which more complex behavior is built?It is now almost 20 y since Schultz, Montague, and Dayan (1) gave a provocative answer to these questions, an answer that is now recognized as one of the cornerstones of reinforcement learning and neuroeconomics. In a series of prior papers (reviewed in ref.2), Schultz and collaborators had demonstrated that dopamine-releasing cells in the ventralThe findings of Kishida et al. directly implicate dopamine in counterfactual learning, regret, and disappointment. midbrain respond to the delivery of rewards with a burst of action potentials, that these cells also respond to cues predicting rewards, and that over the course of learning, dopamine responses shift from the delivery of the reward to the predictive cue itself. Schultz, Montague, and Dayan proposed a simple, but powerful, computational account of these findings: Dopamine encodes a key variable posited by theories of reinforcement learning. These theories posit that animals select behaviors on the basis of which ones they expect to result in reward, updating their beliefs on the difference between expectations and observed outcomes, good or bad (3). This reward prediction error is large when rewards are unexpected and small when rewards are fully predicted, and its magnitude and sign drive the speed and direction of learning, respectively.This hypothesis proved seminal in several ways. First, it linked a particular neural signal to a computational model, thus permitting the development of quantitative predictions about the physiology of reward and learning. Second, because that computational model was a model of both learning and choice, it inspired subsequent studies probing the neurobiology of decision making. Finally, because many drugs of abuse directly affect dopamine function, the model provided a mathematical explanation for addiction. If dopamine encodes a prediction error, and if more dopamine signals that a reward is better than predicted, then every dose of cocaine or methamphetamine is more rewarding than expected, and the cues associated with these chemicals become powerful motivators for drug-seeking behavior. In other words, addiction is a normal learning process gone awry (4). Human Dopamine Recordings Challenge the Reward Prediction Error HypothesisThe dopamine prediction error finding has been endorsed by dozens of neurobiological studies in animals, and indirectly supported by brain imaging studies in humans (5, 6). In PNAS, Kishida et al. (7) build upon this work by directly measuring dopamine release in the human striatum. Their remarkable findings directly challenge the now classic view that dopamine release simply encodes errors in reward prediction and instead suggest that dopamin...

show abstract

Sub-Second Dopamine Detection in Human Striatum

Cited by 105 publications

References 20 publications

Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward

Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward

The Long and the Short of Serotonergic Stimulation: Optogenetic activation of dorsal raphe serotonergic neurons changes the learning rate for rewards

Dopamine: Context and counterfactuals

Contact Info

Product

Resources

About