Non-invasive brain stimulation as a tool to study cerebellar-M1 interactions in humans
Bibliographic record
Abstract
The recent development of non-invasive brain stimulation techniques such as transcranial magnetic stimulation (TMS) has allowed the non-invasive assessment of cerebellar function in humans. Early studies showed that cerebellar activity, as reflected in the excitability of the dentate-thalamo-cortical pathway, can be assessed with paired stimulation of the cerebellum and the primary motor cortex (M1) (cerebellar inhibition of motor cortex, CBI). Following this, many attempts have been made, using techniques such as repetitive TMS and transcranial electrical stimulation (TES), to modulate the activity of the cerebellum and the dentate-thalamo-cortical output, and measure their impact on M1 activity. The present article reviews literature concerned with the impact of non-invasive stimulation of cerebellum on M1 measures of excitability and "plasticity" in both healthy and clinical populations. The main conclusion from the 27 reviewed articles is that the effects of cerebellar "plasticity" protocols on M1 activity are generally inconsistent. Nevertheless, two measurements showed relatively reproducible effects in healthy individuals: reduced response of M1 to sensorimotor "plasticity" (paired-associative stimulation, PAS) and reduced CBI following repetitive TMS and TES. We discuss current challenges, such as the low power of reviewed studies, variability in stimulation parameters employed and lack of understanding of physiological mechanisms underlying CBI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".