Vestibular contribution to balance control in the medial gastrocnemius and soleus
Bibliographic record
Abstract
The soleus (Sol) and medial gastrocnemius (mGas) muscles have different patterns of activity during standing balance and may have distinct functional roles. Using surface electromyography we previously observed larger responses to galvanic vestibular stimulation (GVS) in the mGas compared with the Sol muscle. However, it is unclear whether this difference is an artifact that reflects limitations associated with surface electromyography recordings or whether a compensatory balance response to a vestibular error signal activates the mGas to a greater extent than the Sol. In the present study, we compared the effect of GVS on the discharge behavior of 9 Sol and 21 mGas motor units from freely standing subjects. In both Sol and mGas motor units, vestibular stimulation induced biphasic responses in measures of discharge timing [11 ± 5.0 (mGas) and 5.6 ± 3.8 (Sol) counts relative to the sham (mean ± SD)], and frequency [0.86 ± 0.6 Hz (mGas), 0.34 ± 0.2 Hz (Sol) change relative to the sham]. Peak-to-trough response amplitudes were significantly larger in the mGas (62% in the probability-based measure and 160% in the frequency-based measure) compared with the Sol (multiple P < 0.05). Our results provide direct evidence that vestibular signals have a larger influence on the discharge activity of motor units in the mGas compared with the Sol. More tentatively, these results indicate the mGas plays a greater role in vestibular-driven balance corrections during standing balance.
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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".