Delay-dependent impairment of spatial working memory with inhibition of NR2B-containing NMDA receptors in hippocampal CA1 region of rats
Bibliographic record
Abstract
Hippocampal N-methyl-D-aspartate receptor (NMDAR) is required for spatial working memory. Although evidence from genetic manipulation mice suggests an important role of hippocampal NMDAR NR2B subunits (NR2B-NMDARs) in spatial working memory, it remains unclear whether or not the requirement of hippocampal NR2B-NMDARs for spatial working memory depends on the time of spatial information maintained. Here, we investigate the contribution of hippocampal NR2B-NMDARs to spatial working memory on delayed alternation task in T-maze (DAT task) and delayed matched-to-place task in water maze (DMP task). Our data show that infusions of the NR2B-NMDAR selective antagonists, Ro25-6981 or ifenprodil, directly into the CA1 region, impair spatial working memory in DAT task with 30-s delay (not 5-s delay), but severely impair error-correction capability in both 5-s and 30-s delay task. Furthermore, intra-CA1 inhibition of NR2B-NMDARs impairs spatial working memory in DMP task with 10-min delay (not 30-s delay). Our results suggest that hippocampal NR2B-NMDARs are required for spatial working memory in long-delay task, whereas spare for spatial working memory in short-delay task. We conclude that the requirement of NR2B-NMDARs for spatial working memory is delay-dependent in the CA1 region.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".