Dopamine modulates excitatory transmission to orexin neurons in a receptor subtype-specific manner
Bibliographic record
Abstract
Dopamine (DA) can promote or inhibit consummatory and reward-related behaviors by activating different receptor subtypes in the lateral hypothalamus and perifornical area (LH/PF). Because orexin neurons are involved in reward and localized in the LH/PF, DA may modulate these neurons to influence reward-related behaviors. To determine the cellular mechanism underlying dopaminergic modulation of orexin neurons, the effect of DA on excitatory transmission to these neurons was investigated using in vitro electrophysiology on rat brain slices. We found that low concentrations (0.1–1 µM) of DA increased evoked excitatory postsynaptic current amplitude while decreasing paired-pulse ratio. In contrast, high concentrations (10–100 µM) of DA did the opposite. The excitatory effect of low DA was blocked by the D1 receptor antagonist SCH-23390, whereas the inhibitory effect of high DA was blocked by the D2 receptor antagonist sulpiride. These results indicate distinct roles of D1 and D2 receptors in bidirectional presynaptic modulation of excitatory transmission. DA had stronger effects on isolated synaptic activity than repetitive ones, suggesting that sensitivity to dopaminergic modulation depends on the level of network activity. In orexin neurons from high-fat diet-fed rats, a high concentration of DA was less effective in suppressing repetitive synaptic activity compared with chow controls. Therefore, in diet-induced obesity, intense synaptic inputs may preferentially reach orexin neurons while intermittent signals are inhibited by high DA levels. In summary, our study provides a cellular mechanism by which DA may exert opposite behavioral effects in the LH/PF through bidirectional modulation of orexin neurons via different DA receptors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".