Corticospinal Excitability Quantification During a Visually-Guided Precision Walking Task in Humans: Potential for Neurorehabilitation
Bibliographic record
Abstract
The corticospinal tract has been shown to be involved in normal walking in humans. However, its contribution during more challenging locomotor tasks is still unclear. As the corticospinal tract can be a potential target to promote gait recovery after neurological injury, it is of primary importance to quantify its use during human walking. The aims of the current study were to: (1) quantify the effects of precision walking on corticospinal excitability as compared to normal walking; (2) assess if corticospinal modulation is related to task difficulty or participants’ performance. Sixteen healthy participants walked on a treadmill during 2 tasks: regular walking (simple task) and stepping onto virtual targets (precision task). Virtual targets appeared randomly at 3 different step lengths: preferred, and ±20%. To assess corticospinal excitability, 25 motor evoked potentials (MEPs) were recorded from the tibialis anterior muscle in each task during walking. Performance for each participant (global success score; % of target hit) and task difficulty related to step length adjustments (success score for each step length) were also calculated. MEP size was larger during the precision task in all participants (mean increase of 93% ± 72%; P < .05) compared to the simple task. There was a correlation between MEP facilitation and individual performance ( r = −.64; P < .05), but no difference in MEP size associated with task difficulty ( P > .05). In conclusion, corticospinal excitability exhibits a large increase during the precision task. This effect needs to be confirmed in neurological populations to potentially provide a simple and non-invasive approach to increase corticospinal drive during gait rehabilitation.
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 | 0.001 | 0.002 |
| 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".