Mirror Electromyografic Activity in the Upper and Lower Extremity: A Comparison between Endurance Athletes and Non-Athletes
Bibliographic record
Abstract
During unimanual motor tasks, muscle activity may not be restricted to the contracting muscle, but rather occurs involuntarily in the contralateral resting limb, even in healthy individuals. This phenomenon has been referred to as mirror electromyographic activity (MEMG). To date, the physiological (non-pathological) form of MEMG has been observed predominately in upper extremities (UE), while remaining sparsely described in lower extremities (LE). Accordingly, evidence regarding the underlying mechanisms and modulation capability of MEMG, i.e. the extent of MEMG in dependency of exerted force during unilateral isometric contractions are insufficiently investigated in terms of LE. Furthermore, it still remains elusive if and how MEMG is affected by long-term exercise training. Here, we provide novel quantitative evidence for physiological MEMG in homologous muscles of LE (tibialis anterior (TA), rectus femoris (RF)) during submaximal unilateral dorsiflexion in healthy young adults. Furthermore, endurance athletes (EA, n=11) show a higher extent of MEMG in LE compared to non-athletes (NA, n=11) at high force demands (80%MVC). While the underlying neurophysiological mechanisms of MEMG still remain elusive, our study indicates, at least indirectly, that sport-related long-term training might affect the amount of MEMG during strong isometric contractions specifically in trained limbs. To support this assumption of exercise-induced limb-specific MEMG modulation, future studies including different sports disciplines with contrasting movement patterns and parameters should additionally be performed.
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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 | 0.001 | 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) | 0.002 | 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".