Age and Task-Dependent Modulations in EMG-EMG Coherence during Gait: A Scoping Review
Bibliographic record
Abstract
ABSTRACT Based on electromyography (EMG) recordings, EMG-EMG coherence method provides a practical approach to investigate neural mechanisms involved in locomotion. Although some studies indicated an influence of age and walking conditions on EMG-EMG coherence, no clear consensus emerged from the existing literature. The aim of this scoping review was to map the current literature on EMG-EMG coherence in healthy adults across ages and walking tasks. Six databases (CINAHL, Cochrane Central, CDSR, MEDLINE, Embase, and Web of Science) were searched, resulting in 31 studies included (575 healthy individuals). These studies analyzed EMG-EMG coherence of muscles involved during different locomotor tasks. The results revealed a consensus regarding the decrease in EMG-EMG coherence during walking with aging, particularly in the Beta and Gamma bands, which could be attributed to natural alterations in the corticospinal tract with age. Furthermore, Beta and Gamma EMG-EMG coherence showed an increased tendency during challenging proprioceptive and proactive locomotor tasks, which is interpreted as an enhancement of cortical involvement in gait control. This review also highlights the necessity for future research to examine EMG-EMG coherence in additional frequency bands, such as Alpha, utilizing standardized signal processing techniques and frequency classifications, and to investigate coherence in children across various locomotor tasks. NEW & NOTEWORTHY The EMG-EMG coherence method, based on electromyography recordings, is a practical tool to study neural mechanisms in locomotion. This scoping review explores the effect of age and walking conditions on EMG-EMG coherence during gait in healthy individuals. The results revealed a strong tendency of EMG-EMG coherence to decrease during aging and to increase during challenging proprioceptive and proactive locomotor tasks. This review also highlights gaps knowledge in children, and methodological concerns for coherence assessment.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".