Pontine Functional Connectivity Gradients
Bibliographic record
Abstract
The cerebellum contributes to a wide range of motor and non-motor functions through its extensive connections with much of the cerebral cortex. The pons plays a key role in these connections. It receives inputs from much of the cerebral cortex, and in turn projects to cerebellar cortex. The anatomical organization of corticopontine and pontocerebellar connectivity has been well described, but the functional organization of the pons in humans remains poorly understood. In particular, it is unclear how inputs from different areas are integrated or segregated within it. We sought to characterize the functional organization of the pons based on its functional connectivity with the cerebral and cerebellar cortices. To this end, we used diffusion map embedding, a non-linear dimensionality reduction technique, to reconstruct gradients that reflect the dominant patterns of pontine functional connectivity. Our results reveal a primary gradient in the pons that is organized along rostrocaudal and mediolateral axes. We found that inputs from higher-order cognitive areas of the cerebral cortex converge on rostral and medial pons, whereas inputs from sensorimotor areas converge on the caudal and lateral pons. The organization of this gradient mirrors the unimodal-to-transmodal gradient previously described in the cerebral and cerebellar cortices. The observed patterns align with findings from invasive tract-tracing in non-human animals and extend them to demonstrate that pontine functional organization can be characterized by a complex pattern of segregation and integration. These findings highlight that, rather than serving as a simple relay, the pons may function as an integrative hub within the cortico-ponto-cerebellar pathway.
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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) | 0.002 | 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".