Norepinephrine and inhibitory transmission: Regional diversity and mechanisms of modulation
Bibliographic record
Abstract
Norepinephrine, a stress-related neuromodulator, is a key regulator of synaptic transmission and neuronal activity. While the impact of norepinephrine on excitatory transmission has been frequently discussed, how norepinephrine regulates inhibitory transmission remains poorly understood. Norepinephrine modulates inhibitory synaptic function and the firing property of inhibitory neurons. These norepinephrine effects on inhibitory transmission are complex and often region- and inhibitory neuron subtype-specific. Malfunctioning of the norepinephrine-induced modulation of inhibitory transmission could underlie various brain diseases, especially norepinephrine-related psychiatric and neurodegenerative disorders. In this review, we examine findings on the expression of norepinephrine receptors in inhibitory neurons and norepinephrine-induced modulation of inhibitory transmission across different regions of the central nervous system. Furthermore, we discuss the role of adrenergic receptors, norepinephrine concentrations, signaling and inhibitory neuron subtypes in norepinephrine-induced modulation of inhibitory transmission. Overall, this review highlights inhibitory transmission as a major target of norepinephrine for influencing circuit functions and shaping behavioral outcomes. • Inhibitory transmission is highly sensitive to norepinephrine (NE). • RNAseq analysis revealed differential expression of adrenergic receptors in inhibitory neurons. • NE-related modulation of inhibition is regional-specific. • The impact of NE on inhibitory neurons mediates various network mechanisms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".