Joint-embeddings reveal functional differences in default-mode network architecture between marmosets and humans
Bibliographic record
Abstract
The default-mode network (DMN) is a distributed functional brain system integral for social and higher-order cognition in humans with implications in a myriad of neuropsychological disorders. In this study, we compared the functional architecture of the DMN between humans and marmosets to assess their similarities and differences using joint gradients. This approach permits simultaneous large-scale mapping of functional systems across the cortex of humans and marmosets, revealing evidence of putative homologies between them. In doing so, we find that the DMN architecture of the marmoset exhibits differences along its anterolateral-posterior axis. Specifically, the anterolateral node of the DMN (dorsolateral prefrontal cortex) displayed weak connections and inconsistent connection topographies as compared to its posterior DMN-nodes (posterior cingulate and posterior parietal cortices). We also present evidence that the marmoset medial prefrontal cortex and temporal lobe areas correspond to other macroscopical distributed functional systems that are not part of the DMN. Given the importance of the marmoset as a pre-clinical primate model for higher-order cognitive functioning and the DMN's relevance to cognition, our results suggest that the marmoset may lack the capacity to integrate neural information to subserve cortical dynamics that are necessary for supporting diverse cognitive demands.
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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 | 0.002 |
| 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".