Optic flow modulates electrocortical activity during steady-state treadmill walking
Bibliographic record
Abstract
Locomotion in real-life environments involves gathering visual information about the environment and regulating one's movements accordingly. Optic flow is a central visual cue that pedestrians use to control locomotor speed and direction. Neuroimaging studies examined cortical locomotor control primarily during treadmill walking without optic flow. In the present study, electroencephalography (EEG) data were recorded during a virtual reality treadmill walking task to obtain insights into the neural mechanisms involved in optic flow processing. Twenty-four healthy young participants performed a virtual reality task that involved periods of standing and walking with or without optic flow. Electrocortical activity data were recorded using a 64-channel EEG system and independent component analysis parsed out individual data into maximally independent components. Components from all participants were then grouped and to analyze task-relevant cortical activity, we examined theta, alpha, and beta power across the sensorimotor, parietal, and parieto-occipital regions. Results revealed significant electrocortical modulations across all regions examined. In the theta frequency band, differences between conditions occurred exclusively in the parieto-occipital region, where an increase in power was observed when walking with optic flow relative to walking without optic flow or standing. Modulations in the alpha frequency band occurred in all regions, with a decrease in sensorimotor and parietal power in both walking conditions relative to standing and a decrease in parieto-occipital power exclusively when walking with optic flow. These findings enhance our understanding of the cortical processes involved in locomotor control and provide foundational knowledge to contextualize deficits following neurological conditions.
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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".