Target-specific modulation of the descending prefrontal cortex inputs to the dorsal raphe nucleus by cannabinoids
Bibliographic record
Abstract
Serotonin (5-HT) neurons located in the raphe nuclei modulate a wide range of behaviors by means of an expansive innervation pattern. In turn, the raphe receives a vast array of synaptic inputs, and a remaining challenge lies in understanding how these individual inputs are organized, processed, and modulated in this nucleus to contribute ultimately to the core coding features of 5-HT neurons. The details of the long-range, top-down control exerted by the medial prefrontal cortex (mPFC) in the dorsal raphe nucleus (DRN) are of particular interest, in part, because of its purported role in stress processing and mood regulation. Here, we found that the mPFC provides a direct monosynaptic, glutamatergic drive to both DRN 5-HT and GABA neurons and that this architecture was conducive to a robust feed-forward inhibition. Remarkably, activation of cannabinoid (CB) receptors differentially modulated the mPFC inputs onto these cell types in the DRN, in effect regulating the synaptic excitatory/inhibitory balance governing the excitability of 5-HT neurons. Thus, the CB system dynamically reconfigures the processing features of the DRN, a mood-related circuit believed to provide a concerted and distributed regulation of the excitability of large ensembles of brain networks.
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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".