A cortical circuit for orchestrating oromanual food manipulation
Bibliographic record
Abstract
ABSTRACT The seamless coordination of hands and mouth—whether in humans eating corn on the cob or mice extracting sunflower seeds—represents one of evolution’s most sophisticated motor achievements. Whereas spinal and brainstem circuits implement basic forelimb and orofacial actions, whether there is a specialized cortical circuit that assembles these actions to enable skilled oromanual manipulation remains unclear. Here, we discover a cortical area and its cell-type-specific circuitry that govern oromanual food manipulation in mice. An optogenetic screen of cortical areas and projection neuron types identified a rostral forelimb-orofacial area (RFO), wherein activation of pyramidal tract (PT Fezf2 ) and intratelencephalic (IT PlxnD1 ) neurons induced concerted posture, forelimb and orofacial movements resembling eating. In a freely moving pasta-eating behavior, pharmacological RFO inactivation impaired the sitting posture, hand recruitment, and oromanual coordination in pasta eating. RFO PT Fezf2 and IT PlxnD1 activity was closely correlated with oromanual pasta manipulation and hand-assisted biting. Optogenetic inhibition revealed that PTs Fezf2 regulate dexterous hand and mouth movements while ITs PlxnD1 play a more prominent role in oromanual coordination. RFO forms the hub of an extensive network, with reciprocal connections to cortical forelimb and orofacial sensorimotor areas, as well as insular and visceral areas. Within this cortical network, RFO PTs Fezf2 project unilaterally to multiple subcortical, brainstem and spinal areas associated with forelimb and orofacial control, while ITs PlxnD1 project bilaterally to the entire network and the ventrolateral striatum, and can mediate concurrent forelimb and mouth movement in part through their striatal projection. Together, these findings uncover the cell-type-specific implementation of a cortical circuit that orchestrates oromanual manipulation, essential for skilled feeding.
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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 | 0.000 |
| 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 | 0.000 |
| 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".