Comparison of low frequency repetitive transcranial magnetic stimulation parameters on motor cortex excitability in normal subjects
Bibliographic record
Abstract
Abstract Background/objectives Optimal low frequency repetitive transcranial magnetic stimulation (LF-rTMS) parameters for treating epilepsy and other brain disorders are unknown. To address this question, a systematic study of the effects of LF-rTMS frequency and intensity on cortical excitability was performed. Methods Using a four-period crossover design, subjects were scheduled for four LF-rTMS sessions that were at least four weeks apart. LF-rTMS was delivered as 900 pulses directed at primary motor cortex using four protocols: 0.5 Hz at 90% resting motor threshold (RMT), 0.5 Hz at 110% RMT, 1 Hz at 90% RMT, and 1 Hz at 110% RMT. Motor evoked potential (MEP) amplitude, resting motor threshold (RMT), and cortical silent period (CSP) were measured before, immediately after, and 60 min after LF-rTMS. Each of the four protocols was analyzed separately to compare baseline measurements to those after LF-rTMS. Results None of the four LF-rTMS protocols produced a trend or significant change in MEP amplitude, RMT, or CSP. Conclusion The lack of significant effect from the four LF-rTMS protocols indicates that none produced evidence for alteration of cortical excitability. The direct comparison of four LF-rTMS protocols is distinct to this investigation, as most similar studies were exploratory and studied only one or two protocols. The negative result relates only to the methods used in this investigation and does not indicate that LF-rTMS does not alter cortical excitability with other parameters. These results may be useful when designing additional investigations into the effect of LF-rTMS on epilepsy, other disorders, and cortical excitability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".