Cytoarchitectural characteristics associated with cognitive flexibility in raccoons
Bibliographic record
Abstract
With rates of psychiatric illnesses such as depression continuing to rise, additional preclinical models are needed to facilitate translational neuroscience research. In the current study, the raccoon (Procyon lotor) was investigated due to its similarities with primate brains, including comparable proportional neuronal densities, cortical magnification of the forepaw area, and cortical gyrification. Specifically, we report on the cytoarchitectural characteristics of raccoons profiled as high, intermediate, or low solvers in a multiaccess problem-solving task. Isotropic fractionation indicated that high-solvers had significantly more cells in the hippocampus (HC) than the other solving groups; further, a nonsignificant trend suggested that this increase in cell profile density was due to increased nonneuronal (e.g., glial) cells. Group differences were not observed in the cellular density of the somatosensory cortex. Thionin-based staining confirmed the presence of von Economo neurons (VENs) in the frontoinsular cortex, although no impact of solving ability on VEN cell profile density levels was observed. Elongated fusiform cells were quantified in the HC dentate gyrus where high-solvers were observed to have higher levels of this cell type than the other solving groups. In sum, the current findings suggest that varying cytoarchitectural phenotypes contribute to cognitive flexibility. Additional research is necessary to determine the translational value of cytoarchitectural distribution patterns on adaptive behavioral outcomes associated with cognitive performance and mental health.
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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 | 0.001 | 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) | 0.002 | 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".