Non-maternal nest building behaviours in mice predict bilateral dorsal hippocampal lesion extent
Bibliographic record
Abstract
Lesions and pharmacological inactivation of the hippocampus have long been important tools for assessing the critical role of the hippocampus in learning and memory. Such studies often require a substantial investment of time and resources and, so, a tool for estimating lesion extent and screening animals prior to histological verification would be of considerable utility. Mice with bilateral hippocampal lesions have previously been observed to be deficient at nest building. Therefore, non-maternal nest construction was assessed as a predictor of the extent of hippocampal lesions. Mice with complete bilateral dorsal hippocampal lesions (comprising >50 % of the total volume of both hippocampi) exhibited severe deficits in nest building, failing to shred and/or gather nesting materials. In contrast, incomplete dorsal hippocampal lesions were not sufficient to cause impairments. Overall, among both male and female mice, nest construction score was highly positively correlated with the total volume of intact dorsal hippocampus. Importantly, reduced nesting behaviours could not be explained by gross motor deficits, which were evaluated by running performance on a non-motorized treadmill. Altogether, spontaneous nest building behaviour was confirmed to be a simple, cost-effective, and reliable predictor of bilateral dorsal hippocampal lesion extent in an otherwise healthy mouse strain.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".