Selective Changes in Thin Spine Density and Morphology in Monkey Prefrontal Cortex Correlate with Aging-Related Cognitive Impairment
Bibliographic record
Abstract
Age-associated memory impairment (AAMI) occurs in many mammalian species, including humans. In contrast to Alzheimer's disease (AD), in which circuit disruption occurs through neuron death, AAMI is due to circuit and synapse disruption in the absence of significant neuron loss and thus may be more amenable to prevention or treatment. We have investigated the effects of aging on pyramidal neurons and synapse density in layer III of area 46 in dorsolateral prefrontal cortex of young and aged, male and female rhesus monkeys (Macaca mulatta) that were tested for cognitive status through the delayed non-matching-to-sample (DNMS) and delayed response tasks. Cognitive tests revealed an age-related decrement in both acquisition and performance on DNMS. Our morphometric analyses revealed both an age-related loss of spines (33%, p < 0.05) on pyramidal cells and decreased density of axospinous synapses (32%, p < 0.01) in layer III of area 46. In addition, there was an age-related shift in the distribution of spine types reflecting a selective vulnerability of small, thin spines, thought to be particularly plastic and linked to learning. While both synapse density and the overall spine size average of an animal were predictive of number of trials required for acquisition of DNMS (i.e., learning the task), the strongest correlate of behavior was found to be the head volume of thin spines, with no correlation between behavior and mushroom spine size or density. No synaptic index correlated with memory performance once the task was learned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".