Behaviour-driven <i>Arc</i> expression is greater in dorsal than ventral CA1 regardless of task or sex differences
Bibliographic record
Abstract
Abstract Evidence from genetic, behavioural, anatomical, and physiological study suggests that the hippocampus functionally differs across its longitudinal (dorsoventral or septotemporal) axis. Although, how to best characterize functional and representational differences in the hippocampus across its long axis remains unclear. While some suggest that the hippocampus can be divided into dorsal and ventral subregions that support distinct cognitive functions, others posit that these regions vary in their granularity of representation, wherein spatial-temporal resolution decreases in the ventral (temporal) direction. Importantly, the cognitive and granular hypotheses make distinct predictions on cellular recruitment dynamics under conditions when animals perform tasks with qualitatively different cognitive-behavioural demands. The cognitive function account implies that dorsal and ventral cellular recruitment differs depending on relevant behavioural demands, while the granularity account suggests similar recruitment dynamics regardless of the nature of the task performed. Here, we quantified cellular recruitment with the immediate early gene (IEG) Arc across the entire longitudinal CA1 axis in female and male rats performing spatial- and fear-guided memory tasks. Our results show that recruitment is greater in dorsal than ventral CA1 regardless of task or sex. This experimentum crucis leads to the strong inference that the granularity hypothesis for functional differences across the longitudinal axis in the rodent hippocampus is correct.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".