Alpha 2 adrenergic receptors on GABAergic, putative sleep‐promoting basal forebrain neurons
Bibliographic record
Abstract
The basal forebrain plays an important role in the modulation of cortical activity and sleep-wake states. Yet its role must be multivalent as lesions reportedly diminish cortical fast activity and also cortical slow activity along with slow wave sleep (SWS). Basal forebrain cholinergic vs. GABAergic cell groups could differentially influence these processes. By labelling recorded neurons with Neurobiotin (Nb) using the juxtacellular technique and identifying them by immunostaining, we previously found that whereas all cholinergic cells increased their firing, the majority of GABAergic neurons decreased their firing in association with evoked cortical activation in urethane-anaesthetized rats. Here, we examined the possibility that such GABAergic, cortical activation 'off' cells might bear alpha 2 adrenergic receptors (alpha2AR) through which noradrenaline (NA) could inhibit them during cortical activation. First using simple dual-immunostaining for glutamic acid decarboxylase (GAD) and the alpha2AAR, we found that the majority (approximately 60%) of GAD-immunopositive (GAD+) neurons through the magnocellular preoptic nucleus (MCPO) and substantia innominata (SI) were labelled for the alpha2AAR. Second, in urethane-anaesthetized rats, we examined whether Nb-labelled, GAD+ cortical activation 'off' neurons that discharged maximally in association with cortical slow wave activity, were immunopositive for alpha2AAR. We found that all the Nb+/GAD+'off' cells were labelled for the alpha2AAR. Such cells could be inhibited in association with cortical activation and waking when noradrenergic locus coeruleus (LC) neurons discharge and be disinhibited with cortical slow waves and SWS when these neurons become inactive. We thus propose that alpha2AR-bearing GABAergic basal forebrain neurons constitute sleep-active and sleep-promoting neurons.
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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".