Neural Control of Superficial and Deep Neck Muscles in Humans
Bibliographic record
Abstract
Human neck muscles have a complex multi-layered architecture. The role and neural control of these neck muscles were examined in nine seated subjects performing three series of isometric neck muscle contractions: 50-N contractions in eight fixed horizontal directions, 25-N contractions, and 50-N contractions, both with a continuously changing horizontal force direction. Activity in the left sternocleidomastoid, trapezius, levator scapulae, splenius capitis, semispinalis capitis, semispinalis cervicis, and multifidus muscles was measured with wire electrodes inserted at the C(4)/C(5) level under ultrasound guidance. We hypothesized that deep and superficial neck muscles would function as postural and focal muscles, respectively, and would thus be controlled by different neural signals. To test these hypotheses, electromyographic (EMG) tuning curves and correlations in the temporal and frequency domains were computed. Three main results emerged from these analyses: EMG tuning curves from all muscles exhibited well-defined preferred directions of activation for the 50-N isometric forces, larger contractions (25 vs. 50 N) yielded more focused EMG tuning curves, and agonist neck muscles from all layers received a common neural drive in the range of 10-15 Hz. The current results demonstrate that all neck muscles can exhibit phasic activity during isometric neck muscle contractions. Similar oscillations in the EMG of neck muscles from different layers further suggest that neck motoneurons were activated by common neurons. The reticular formation appears a likely generator of the common drive to the neck motoneurons due to its widespread projections to different groups of neck motoneurons.
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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 | 0.000 | 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".