Active versus resting neuro‐navigated robotic transcranial magnetic stimulation motor mapping
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) motor mapping is a safe, non-invasive method that can be used to study corticomotor organization. Motor maps are typically acquired at rest, and comparisons to maps obtained during muscle activation have been both limited and contradictory. Understanding the relationship between functional activation of the corticomotor system as recorded by motor mapping is crucial for their use clinically and in research. The present study utilized robotic TMS paired with personalized neuro-navigation to examine the relationship between resting and active motor map measures and their relationship with motor performance. Twenty healthy right-handed participants underwent resting and active robotic TMS motor mapping of the first dorsal interosseous to 10% maximum voluntary contraction. Motor map parameters including map area, volume, and measures of map centrality were compared between techniques using paired sample tests of difference and Bland-Altman plots and analysis. Map area, volume, and hotspot magnitude were larger in the active motor maps, while map center of gravity and hotspot locations remained consistent between both maps. No associations were observed between motor maps and motor performance as measured by the Purdue Pegboard Test. Our findings support previous suggestions that maps scale with muscle contraction. Differences in mapping outcomes suggest rest and active motor maps may reflect functionally different corticomotor representations. Advanced analysis methods may better characterize the underlying neurophysiology of both types of motor mapping.
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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 | 0.002 |
| 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".