The medium latency muscle response to a vestibular perturbation is increased after depression of the cerebellar vermis
Bibliographic record
Abstract
Abstract Introduction Galvanic vestibular stimulation (GVS) is able to evoke distinct responses in the muscles used for balance. These reflexes, termed the short (SL) and medium latency (ML) responses, can be altered by sensory input; decreasing in size when additional sensory cues are available. Although much is known about these responses, the origin and role of the responses are still not fully understood. It has been suggested that the cerebellum, a structure that is involved in postural control and sensory integration, may play a role in the modulation of these reflexes. Methods The cerebellar vermis was temporarily depressed using continuous theta burst stimulation and SL, ML and overall vestibular electromyographic and force plate shear response amplitudes were compared before and after cerebellar depression. Results There were no changes in force plate shear amplitude and a non‐significant increase for the SL muscle response (p = .071), however, we did find significant increases in the ML and overall vestibular muscle response amplitudes after cerebellar depression (p = .026 and p = .016, respectively). No changes were evoked when a SHAM stimulus was used. Discussion These results suggest that the cerebellar vermis plays a role in the modulation of vestibular muscle reflex responses to GVS.
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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) | 0.002 | 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".