A novel protocol to investigate motor training-induced plasticity and sensorimotor integration in the cerebellum and motor cortex
Bibliographic record
Abstract
Our group set out to develop a sensitive technique, capable of detecting output changes from the posterior fossa following a motor acquisition task. Transcranial magnetic stimulation (TMS) was applied over the right cerebellar cortex 5 ms in advance of test stimuli over the left cerebral motor cortex (M1), suppressing test motor-evoked potentials (MEPs) recorded in a distal hand muscle. Ten participants typed the letters Z, D, F, and P in randomized 8-letter sequences for ∼15 min, and 10 participants took part in the control condition. Cerebellar-M1 recruitment curves were established before and after the motor acquisition task. Cerebellar inhibition at 50% (CBI50) was defined as the intensity of cerebellar-M1 stimulations that produced MEPs that were 50% of the initial test MEP. Collection also occurred at stimulator intensities 5 and 10% above CBI50. A significant interaction effect of group (experimental and control) vs. time (pre- and postintervention) was observed [F(1,18) = 4.617, P = 0.046]. Post hoc tests showed a significant effect for the learning task in the experimental group [F(1,9) = 10.28, P = 0.01]. Further analysis showed specific disinhibition at CBI50 (P = 0.04), CBI50+5% (P = 0.008), and CBI50+10% (P = 0.01) for the experimental group only. Reaction time (P < 0.001) and accuracy (P = 0.006) improved significantly following practice, implying that disinhibition coincides with motor learning. No changes, however, were seen in the control condition. We conclude that this protocol is a sensitive technique that may be used to study cerebellar disinhibition with motor acquisition in vivo.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".