Cortical dynamics underlying initiation of rapid steps with contrasting postural demands
Bibliographic record
Abstract
Our ability to flexibly initiate rapid visually-guided stepping movements can be measured in the form of express visuomotor responses (EVRs), which are short-latency (∼100 ms), goal-directed bursts of lower-limb muscle activity. Interestingly, we previously demonstrated that recruitment of anticipatory postural adjustments (APAs) interacted with the subcortically-generated EVRs in the lower limb, suggesting context-dependent top-down modulation. We investigated the associated cortical dynamics prior to and during rapid step initiation towards a salient visual target in twenty-one young, healthy individuals while stepping under varying postural demands. We recorded high-density EEG, surface electromyography from gluteus medius and ground-reaction forces. Independent component analysis and time-frequency statistics revealed significant, yet relatively modest differences between conditions in preparatory cortical dynamics, most evidently in primary motor areas. Following target presentation, we observed stronger theta and alpha power enhancement in the supplementary motor area, and stronger alpha and beta power decrease in primary motor, parietal and occipital clusters during APA recruitment that preceded steps under high postural demands. Side-specific changes in motor cortex lagged the timing of EVR expression, supporting the EVR's purportedly subcortical origin. Together, our findings point towards greater cortical involvement in step initiation under high postural demands as compared to more reflexive, stimulus-driven steps. These findings may be particularly relevant for populations where postural control is impaired by age or disease, as more cortical resources may need to be allocated during stepping.
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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".