Divergence of rodent and primate medial frontal cortex functional connectivity
Bibliographic record
Abstract
With the medial frontal cortex (MFC) centrally implicated in several major neuropsychiatric disorders, it is critical to understand the extent to which MFC organization is comparable between humans and animals commonly used in preclinical research (namely rodents and nonhuman primates). Although the cytoarchitectonic structure of the rodent MFC has mostly been conserved in humans, it is a long-standing question whether the structural analogies translate to functional analogies. Here, we probed this question using ultra high field fMRI data to compare rat, marmoset, and human MFC functional connectivity. First, we applied hierarchical clustering to intrinsically define the functional boundaries of the MFC in all three species, independent of cytoarchitectonic definitions. Then, we mapped the functional connectivity "fingerprints" of these regions with a number of different brain areas. Because rats do not share cytoarchitectonically defined regions of the lateral frontal cortex (LFC) with primates, the fingerprinting method also afforded the unique ability to compare the rat MFC and marmoset LFC, which have often been suggested to be functional analogs. The results demonstrated remarkably similar intrinsic functional organization of the MFC across the species, but clear differences between rodent and primate MFC whole-brain connectivity. Rat MFC patterns of connectivity showed greatest similarity with premotor regions in the marmoset, rather than dorsolateral prefrontal regions, which are often suggested to be functionally comparable. These results corroborate the viability of the marmoset as a preclinical model of human MFC dysfunction, and suggest divergence of functional connectivity between rats and primates in both the MFC and LFC.
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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 | 0.001 |
| 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) | 0.001 | 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".