What have We Learned from “Perturbing” the Human Cortical Motor System with Transcranial Magnetic Stimulation?
Bibliographic record
Abstract
The purpose of this paper is twofold. First, we will review different approaches that one can use with transcranial magnetic stimulation (TMS) to study both its effects on motor behavior and on neural connections in the human brain. Second, we will present evidence obtained in TMS-based studies showing that the dorsal premotor area (PMd), the ventral premotor area (PMv), the supplementary motor area (SMA), and the pre-supplementary motor area (pre-SMA) each have different roles to play in motor behavior. We highlight the importance of the PMd in response selection based on arbitrary cues and in the control of arm movements, the PMv in grasping and in the discrimination of bodily actions, the SMA in movement sequencing and in bimanual coordination, and the pre-SMA in cognitive control. We will also discuss ways in which TMS can be used to chart "true" cerebral reorganization in clinical populations and how TMS might be used as a therapeutic tool to facilitate motor recovery after stroke. We will end our review by discussing some of the methodological challenges and future directions for using this tool in basic and clinical neuroscience.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".