Group 1 Metabotropic Glutamate Receptor Function and Its Regulation of Learning and Memory in the Aging Brain
Bibliographic record
Abstract
Normal aging is generally characterized by a slow decline of cognitive abilities albeit with marked individual differences. Several animal models have been studied to explore the molecular and cellular mechanisms underlying this phenomenon. The excitatory neurotransmitter glutamate and its receptors have been closely linked to spatial learning and hippocampus-dependent memory processes. For decades, ionotropic glutamate receptors have been known to play a critical role in synaptic plasticity, a form of adaptation regulating memory formation. Over the past 10 years, several groups have shown the importance of group 1 metabotropic glutamate receptor (mGluR) in successful cognitive aging. These G-protein-coupled receptors are enriched in the hippocampal formation and interact physically with other proteins in the membrane including glutamate ionotropic receptors. Synaptic plasticity is crucial to maintain cognitive abilities and long-term depression (LTD) induced by group 1 mGluR activation, which has been linked to memory in the aging brain. The translation and synthesis of proteins by mGluR-LTD modulate ionotropic receptor trafficking and expression of immediate early genes related to cognition. Fragile X syndrome, a genetic form of autism characterized by memory deficits, has been associated to mGluR receptor malfunction and aberrant activation of its downstream signaling pathways. Dysfunction of mGluR could also be involved in neurodegenerative disorders like Alzheimer's disease (AD). Indeed, beta-amyloid, the main component of insoluble senile plaques and one of the hallmarks of AD, occludes mGluR-dependent LTD leading to diminished functional synapses. This review highlights recent findings regarding mGluR signaling, related synaptic plasticity, and their potential involvement in normal aging and neurological disorders.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".