Blockade of NMDA receptors 2A subunit in the dorsal striatum impairs the learning of a complex motor skill.
Bibliographic record
Abstract
Accumulating evidence proposes that the striatum, known to control voluntary movement, may also play a role in learning and memory. Striatum learning is thought to require long-lasting reorganization of striatal circuits and changes in the strength of synaptic connections during the memorization of a complex motor task. Whether the ionotropic glutamate receptor N-methyl-D-aspartate (NMDAR) contributes to the molecular mechanisms of these memory processes is still unclear. The aim of the present study was to investigate the role of striatal NMDAR and its subunit composition during the learning of the accelerating rotarod task in mice. To this end, we injected directly into the dorsal striatum of mice, via chronically implanted cannula, the NMDAR channel blocker MK-801 as well as the NR2A and NR2B subunit-selective antagonists NVP-AAM077 and Ro 25-6981, respectively, before rotarod training. There was no effect in the motor performances of mice treated with 1.0 μg/side of MK-801, 0.1 μg/side of NVP-AAM077, or 5 and 10 μg/side of Ro 25-6981. In contrast, injections of 2.5 and 5 μg/side of MK-801 or 0.5 and 1 μg/side of NVP-AAM077 impaired motor learning at Day 3 and 8. Interestingly, treatments with MK-801 and NVP-AAM077 did not alter the general motor capacities of mice as revealed by the stepping, wire suspension, and pole tests. Our study demonstrates that the NMDAR of the dorsal striatum contributes to motor learning, especially during the slow acquisition phase, and that NR2A subunits play a critical role in this process.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".