Very low amplitude muscle activity increases probability of motor evoked potentials in healthy individuals and in ALS
Bibliographic record
Abstract
Abstract Introduction In many clinical and research settings, transcranial magnetic stimulation (TMS) intensities are standardised based on resting motor threshold (RMT). It is well-established that contraction of the target muscle increases motor evoked potential (MEP) amplitude and correspondingly decreases RMT. As such, when estimating RMT it is crucial to ensure the target muscle is relaxed. Typically trials in which baseline electromyographic (EMG) amplitude exceeds a specified threshold are rejected. The influence of motor activity below typical rejection thresholds on MEP amplitudes has yet to be established. Methods We retrospectively analysed TMS-EMG data collected during RMT measurement in 45 healthy controls (1761 datapoints) and 35 people with amyotrophic lateral sclerosis (ALS, 1238 datapoints). Trials where root mean squared (RMS) baseline EMG amplitude exceeded 10 µV were rejected. Generalised linear mixed-effects models were used to assess effects of muscle activity below this rejection threshold on probability of evoking an MEP with peak-to-peak amplitude ≥50 µV . Results Greater sub-rejection-threshold activity significantly increases MEP probability in controls (p<0.0004) and people with ALS (p=0.0010). Models predicted a 31-38% increase in MEP probability when baseline RMS-EMG amplitude increased from 1 µV to 9 µV . Sub-rejection– threshold baseline activity was significantly greater in ALS than controls (p=0.0055). Discussion We have shown for the first time that higher EMG amplitudes below a typical rejection threshold markedly increase probability of evoking an MEP with peak-to-peak amplitude ≥50 µV . Researchers should take measures to account for effects of sub-rejection–threshold activity on RMT, particularly in populations where baseline activity may be elevated, such as in ALS.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".