Spinal and cortical premotor control of primate dexterous hand movements
Bibliographic record
Abstract
Abstract Skilled hand movements are an evolutionary advance in primates. Phylogenetically distinct corticospinal pathways are involved in hand control: “newer” direct corticomotoneuronal (CM) pathways and “older” indirect corticospinal pathways mediated by spinal premotor interneurons (PreM-INs). However, the functional differences of these pathways remain unclear. Here we show that CM cells and PreM-INs have distinct physiological properties for the activation of hand muscles and provide different types of movement control signals while monkeys perform a precision grip task. Spike-triggered averaging of electromyographic activity indicated that PreM-INs coactivate a larger number of muscles, whereas CM cells more selectively control fewer muscles. The firing activity of PreM-INs was tightly correlated with their target muscle activity and had a greater contribution to generating hand muscle activity. In contrast, CM cell activity diverged temporally from the target muscle activity and had a smaller contribution to its generation. On the basis of these results, we hypothesize that PreM-INs produce gross muscle activity by activating synergistic muscles, whereas CM cells fine-tune target muscle activity. This idea was supported by dimensional reduction analyses of hand muscle activity, as PreM-IN activity was specifically correlated with lower dimensional control of muscle activity, and CM cell activity was correlated with higher dimensional control. These results indicate that the two pathways have distinct functions, synergistic control and fine tuning of hand muscle activity, both of which are essential for the development of dexterous hand movement in primates.
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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".