Contribution of NR2A and NR2B NMDA subunits to bidirectional synaptic plasticity in the hippocampus in vivo
Bibliographic record
Abstract
It has recently been proposed that activation of the NR2A subunit results in Long-term potentiation (LTP) induction, whereas activation of the NR2B subunit results in long-term depression (LTD) induction. The present study undertakes to replicate these findings in vivo to determine if a role for specific subunits in synaptic plasticity can be shown in the intact brain. Field recordings were made from the CA1 subfield of the hippocampus using Schaffer collateral stimulation in anesthetized male Sprague-Dawley rats. Antagonists of the N-methyl-D-aspartate receptors NR2A and NR2B subunits were administered by either intraperitoneal (i.p.) or intrahippocampal (i.h.) injections to assess their involvement in LTP (100 Hz stimuli) and LTD (200 Paired-burst stimuli). i.h. injection of Ro25-6981 (100 microM) significantly attenuated hippocampal LTP expression and completely blocked LTD expression. When administered i.p., Ro25-6981 (6 mg/kg) again blocked LTD, but did not significantly diminish the expression of LTP. When NVP-AAM077 was administered i.h. (80 microM) both LTP and LTD were completely abolished. The administration of this compound i.p. (1.2 mg/kg) also significantly attenuated LTP, but did not affect LTD. These data suggest that both NR2A and NR2B subunits can play roles in LTP and LTD in the hippocampus in vivo.
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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".