Calretinin-Expressing Neurons in the Basal Forebrain Specifically Contact Granule Cells in the Olfactory Bulb and Modulate Odor Learning
Bibliographic record
Abstract
GABAergic neurons in basal forebrain (BF) nuclei project densely to all layers of the mouse main olfactory bulb (OB), the first relay in odor information processing. However, BF projection neurons are diverse, and the contribution of each subtype to odor information processing is not known. In the present study, we used retrograde and anterograde tracing methods together with whole-brain light-sheet analyses, patch-clamp recordings coupled with optogenetic and chemogenetic approaches during spontaneous odor discrimination, and go/no-go odor discrimination/learning tests to characterize the synaptic targets in the OB of BF calretinin-expressing (CR+) GABAergic cells and to reveal their functional implications. We used mice of either sex to show that OB-projecting CR+ neurons innervate the bulbar granule cell (GC) layer but not the glomerular layer. Optogenetic stimulation of CR+ axonal projections in OB slices elicited monosynaptic GABAergic currents in GCs. Retrograde rabies virus-based transsynaptic tracing experiments confirmed these synaptic connections and further suggested that CR+ neurons provide the principal, if not the unique, BF input onto GCs. Chemogenetic inhibition of CR+ neurons in the BF of male mice did not affect odor discrimination in habituation/dishabituation tasks but led to impairment in odor learning during go/no-go odor-associative tasks. Our results revealed a subtype-specific projection pattern in the OB of a select population of BF neurons and suggested that distinct BF GABAergic projections have distinct effects on odor information processing and learning.
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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) | 0.000 | 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".