Bilateral intracortical inhibition during unilateral motor preparation and sequence learning
Bibliographic record
Abstract
Motor sequence learning gradually quickens reaction time, suggesting that sequence learning alters motor preparation processes. Interestingly, evidence has shown that preparing sequence movements decreases short intracortical inhibition (SICI) in the contralateral motor cortex (M1), but also that sequence learning alters motor preparation processes in both the contralateral and ipsilateral M1s. Therefore, one possibility is that sequence learning alters the SICI decreases occurring during motor preparation in bilateral M1s. To examine this, two novel hypotheses were tested: unilateral sequence preparation would decrease SICI in bilateral M1s, and sequence learning would alter such bilateral SICI responses. Paired-pulse transcranial magnetic stimulation was delivered over the contralateral and ipsilateral M1s to assess SICI in an index finger muscle during the preparation of sequences initiated by either the right index or little finger. In the absence of sequence learning, SICI decreased in both the contralateral and ipsilateral M1s during the preparation of sequences initiated by the right index finger, suggesting that SICI decreases in bilateral M1s during unilateral motor preparation. As sequence learning progressed, SICI decreased in the contralateral M1 whilst it increased in the ipsilateral M1. Moreover, these bilateral SICI responses were observed at the onset of motor preparation, suggesting that sequence learning altered baseline SICI levels rather than the SICI decreases occurring during motor preparation per se. Altogether, these results suggest that SICI responses in bilateral M1s reflect two motor processes: an acute decrease of inhibition during motor preparation, and a cooperative but bidirectional shift of baseline inhibition levels as sequence learning progresses.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".