Mapping of fine‐scale rat prefrontal cortex connections: Evidence for detailed ordering of inputs and outputs connecting the temporal cortex and sensory‐motor regions
Bibliographic record
Abstract
Cerebral cortex structure is crucially important for cortical organization and function. The organization of prefrontal cortex (PFC) is controversial and here we seek to understand it more clearly through the study of fine-scale cortical connections. To determine the ordering of microscale input and output connections in the rat PFC, we injected small volumes (20-30 nl) of anterograde (Fluro-Ruby) and retrograde (Fluoro-Gold) neuroanatomical tracers into PFC. These injections revealed several connected regions of the brain but here we report findings restricted to PFC to temporal cortex and sensory-motor cortex pathways. In agreement with previous studies incorporating larger injection volumes we found that smaller injection volumes revealed a more detailed, fine-scale ordering of both prefrontal inputs and output connections to the temporal cortex and sensory-motor cortex regions. These findings are also supported by labelling observed from additional tracer injections made into corresponding regions of temporal cortex. The topography observed reflected the ordering seen at a larger level (i.e., with larger injection volumes) but there were some differences in the topography, such as in relation to the direction of ordering. In agreement with earlier work, we found that fine-scale input and output connections were not always aligned with respect to one another. These results provide evidence for topographically arranged inputs and outputs in two distinct PFC pathways, along with evidence for different connectional patterns within the same pathways. Based on theories of functional connectivity, these findings provide evidence for prefrontal cortical regions residing within networks that contribute to different cognitive functions.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".