Hippocampal up‐regulation of NCAM expression and polysialylation plays a key role on spatial memory
Bibliographic record
Abstract
Memory formation has been associated with structural and functional modifications of synapses. Cell adhesion molecules are prominent modulators of synaptic plasticity. Here, we investigated the involvement of the cell adhesion molecules, NCAM, its polysialylated state (PSA-NCAM) and L1 in spatial learning-induced synaptic remodeling and memory storage. A differential regulation of these adhesion molecules was found in the hippocampus of rats submitted to one training session in the spatial, but not cued, version of the Morris water maze. Twenty-four hours after training, synaptic expression of NCAM and PSA-NCAM was increased, whereas L1 appeared markedly decreased. The regulation of these molecules was spatial learning-specific, except for L1 reduction, which could be attributed to swimming under stressful conditions rather than to learning. Subsequent psychopharmacological experiments were performed to address the functional role of NCAM and PSA-NCAM in the formation of spatial memories. Rats received an intracerebroventricular injection of either a synthetic peptide (C3d) aimed to interfere with NCAM function, or endoneuraminidase, an enzyme that cleaves polysialic acid from NCAM. Both treatments affected acquisition of spatial information and lead to impaired spatial memory abilities, supporting a critical role of the observed learning-induced up-regulation of synaptic NCAM expression and polysialylation on spatial learning and memory. Therefore, our findings highlight NCAM as a learning-modulated molecule critically involved in the hippocampal remodeling processes underlying spatial memory formation.
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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".