Effect of Low-Frequency Repetitive Transcranial Magnetic Stimulation on Interhemispheric Inhibition
Bibliographic record
Abstract
We studied the effects of 1-Hz repetitive transcranial magnetic stimulation (rTMS) on the excitability of interhemispheric connections in 13 right-handed healthy volunteers. TMS was performed using figure-eight coils, and surface electromyography (EMG) was recorded from both first dorsal interosseous (FDI) muscles. A paired-pulse method with a conditioning stimulus (CS) to the motor cortex (M1) followed by a test stimulus to the opposite M1 was used to study the interhemispheric inhibition (ppIHI). Both CS and TS were adjusted to produce motor-evoked potentials of approximately 1 mV in the contralateral FDI muscles. After baseline measurement of right-to-left IHI (pre-RIHI) and left-to-right IHI (pre-LIHI), rTMS was applied over left M1 at 1 Hz with 900 stimuli at 115% of resting motor threshold. After rTMS, ppIHI was studied using both the pre-rTMS CS (post-RIHI and post-LIHI) and an adjusted post-rTMS CS set to produce 1-mV motor evoked potentials (MEPs; post-RIHI(adj) and post-LIHI(adj)). The TS was set to produce 1-mV MEPs. There was a significant reduction in post-LIHI (P = 0.0049) and post-LIHI(adj) (P = 0.0169) compared with pre-LIHI at both interstimulus intervals of 10 and 40 ms. Post-RIHI was significantly reduced compared with pre-RIHI (P = 0.0015) but pre-RIHI and post-RIHI(adj) were not significantly different. We conclude that 1-Hz rTMS reduces IHI in both directions but is predominantly from the stimulated to the unstimulated hemisphere. Low-frequency rTMS may be used to modulate the excitability of IHI circuits. Treatment protocols using low-frequency rTMS to reduce cortical excitability in neurological and psychiatric conditions need to take into account their effects on IHI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".