Dorsal raphe stimulation relays a reward signal to the ventral tegmental area via GluN2C NMDA receptors
Bibliographic record
Abstract
BACKGROUND: Glutamate relays a reward signal from the dorsal raphe (DR) to the ventral tegmental area (VTA). However, the role of the different subtypes of N-methyl-D-aspartate (NMDA) receptors is complex and not clearly understood. Therefore, we measured NMDA receptors subunits expression in limbic brain areas. In addition, we studied the effects of VTA down-regulation of GluN2C NMDA receptor on the reward signal that arises from DR electrical stimulation. METHODS: Using qPCR, we identified the relative composition of the different Grin2a-d subunits of the NMDA receptors in several brain areas. Then, we used fluorescent in situ hybridization (FISH) to evaluate the colocalization of Grin2c and tyrosine hydroxylase (TH) mRNA in VTA neurons. To assess the role of GluN2C in brain stimulation reward, we downregulated this receptor using small interfering RNA (siRNA) in rats self-stimulating for electrical pulses delivered to the DR. To delineate further the specific role of GluN2C in relaying the reward signal, we pharmacologically altered the function of VTA NMDA receptors by bilaterally microinjecting the NMDA receptor antagonist PPPA. RESULTS: We identified GluN2C as the most abundant subunit of the NMDA receptor expressed in the VTA. FISH revealed that about 50% of TH-positive neurons colocalize with Grin2c transcript. siRNA manipulation produced a selective down-regulation of the GluN2C protein subunit and a significant reduction in brain stimulation reward. Interestingly, PPPA enhanced brain stimulation reward, but only in rats that received the nonactive RNA sequence. CONCLUSION: The present results suggest that VTA glutamate neurotransmission relays a reward signal initiated by DR stimulation by acting on GluN2C NMDA 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".