Corticospinal contribution to arm muscle activity during human walking
Bibliographic record
Abstract
When we walk, our arm muscles show rhythmic activity suggesting that the central nervous system contributes to the swing of the arms. The purpose of the present study was to investigate whether corticospinal drive plays a role in the control of arm muscle activity during human walking. Motor evoked potentials (MEPs) elicited in the posterior deltoid muscle (PD) by transcranial magnetic stimulation (TMS) were modulated during the gait cycle in parallel with changes in the background EMG activity. There was no significant difference in the size of the MEPs at a comparable level of background EMG during walking and during static PD contraction. Short latency intracortical inhibition (SICI; 2 ms interval) studied by paired-pulse TMS was diminished during bursts of PD EMG activity. This could not be explained only by changes in background EMG activity and/or control MEP size, since SICI showed no correlation to the level of background EMG activity during static PD contraction. Finally, TMS at intensity below the threshold for activation of corticospinal tract fibres elicited a suppression of the PD EMG activity during walking. Since TMS at this intensity is likely to only activate intracortical inhibitory interneurones, the suppression is in all likelihood caused by removal of a corticospinal contribution to the ongoing EMG activity. The data thus suggest that the motor cortex makes an active contribution, through the corticospinal tract, to the ongoing EMG activity in arm muscles during walking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".