Sensorimotor gating and spatial learning in α7‐nicotinic receptor knockout mice
Bibliographic record
Abstract
The role of acetylcholine and specific nicotinic receptors in sensorimotor gating and higher cognitive function has been controversial. Here, we used a commercially available mouse with a null mutation in the Chrna7(tm1Bay) gene [α7-nicotinic acetylcholine receptor (nAChR) knockout (KO) mouse] in order to assess the role of the α7-nAChR in sensorimotor gating and spatial learning. We examined prepulse inhibition (PPI) of startle and nicotine-induced enhancement of PPI. We also tested short- and long-term habituation of the startle response as well as of locomotor behaviour in order to differentiate the role of this receptor in the habituation of evoked behaviour (startle) vs. motivated behaviour (locomotion). To address higher cognition, mice were also tested in a spatial learning task. Our results showed a mild but consistent PPI deficit in α7-nAChR KO mice. Furthermore, they did not show nicotine-induced enhancement of startle or PPI. Short- and long-term habituation was normal in KO mice for both types of behaviours, evoked or motivated, and they also showed normal learning and memory in the Barnes maze. Thorough analysis of the behavioural data indicated a slightly higher degree of anxiety in α7-nAChR KO mice; however, this could only be partially confirmed in an elevated plus maze test. In summary, our data suggest that α7-nAChRs play a minor role in PPI, but seem to mediate nicotine-induced PPI enhancement. We found no evidence to suggest that they are important for habituation or spatial 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".