Transcranial Magnetic Stimulation during Gait: A Review of Methodological and Technological Challenges
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) is widely used for therapeutic and research purposes such as cognitive studies, treatment of psychiatric disorders, and Parkinson's disease. In research, TMS is perhaps the only technique that can establish a functional connection between brain regions and task performance. In gait research, often TMS is used to identify the extent to which leg motor cortex is involved in different phases on gait cycle. However, using TMS in gait can be challenging for several technical reasons and physiological variations. The objective of this narrative review is to summarize literature in the field of TMS and gait research and present comprehensive challenges. A comprehensive literature search was conducted in PubMed and Google Scholar to identify all relevant literature on TMS and gait. Several critical challenges could potentially impact the findings. For instance, the use of different protocols to obtain motor threshold. This review presents some of the critical challenges in applying TMS during gait. It is important to be aware of these variations and utilize strategies to mitigate some challenges.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".