European Journal of NeuroscienceNeurosciencePeer-reviewed journal article
Slow-rising and fast-falling dopaminergic dynamics jointly adjust negative prediction error in the ventral striatum
Yu ShikanoiD, Sho YagishitaiD, Kenji F. TanakaiD, Norio TakataiD
Abstract
The greater the reward expectations are, the more different the brains physiological response will be. Although it is well-documented that better-than-expected outcomes are encoded quantitatively via midbrain dopaminergic (DA) activity, it has been less addressed experimentally whether worse-than-expected outcomes are expressed quantitatively as well. We show that larger reward expectations upon unexpected reward omissions are associated with the preceding slower rise and following larger decrease (DA dip) in the DA concentration at the ventral striatum of mice. We set up a lever press task on a fixed ratio (FR) schedule requiring five lever presses as an effort for a food reward (FR5). The mice occasionally checked the food magazine without a reward before completing the task. The percentage of this premature magazine entry (PME) increased as the number of lever presses approached five, showing rising expectations with increasing proximity to task completion, and hence greater reward expectations. Fiber photometry of extracellular DA dynamics in the ventral striatum using a fluorescent protein (genetically encoded GPCR-activation-based-DA sensor: GRABDA2m) revealed that the slow increase and fast decrease in DA levels around PMEs were correlated with the PME percentage, demonstrating a monotonic relationship between the DA dip amplitude and degree of expectations. Computational modeling of the lever press task implementing temporal difference errors and state transitions replicated the observed correlation between the PME frequency and DA dip amplitude in the FR5 task. Taken together, these findings indicate that the DA dip amplitude represents the degree of reward expectations monotonically, which may guide behavioral adjustment.
- organism: mouse
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 4
- FWCI
- 0.48
- Field percentile
- top 47%
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