Transcranial Magnetic Stimulation in Different Current Directions Activates Separate Cortical Circuits
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) to the primary motor cortex (M1) produces a series of corticospinal descending waves, with a direct (D) wave followed by several indirect (I) waves. TMS inducing posterior-anterior (PA) current in the brain predominantly recruits the early I1-wave, whereas anterior-posterior (AP) directed current preferentially recruits the late I3-wave. However, it is not known whether I-waves elicited by different current directions are mediated by the same neuronal populations. We studied the neuronal mechanisms mediating I-waves by examining the influence of short-latency afferent inhibition (SAI) on various I-waves. SAI was tested with electrical median nerve stimulation at the wrist followed by TMS to the contralateral M1 at different current directions. Surface electromyograms and single motor units were recorded from the first dorsal interosseous muscle. SAI was weaker for the AP compared with that for the PA current direction. With increasing median nerve stimulation intensities, SAI increased for the PA direction but showed a U-shaped relationship for the AP direction. SAI produced more inhibition of late I-waves generated by PA than those generated by AP current direction. We conclude that late I-waves generated by PA and AP current directions are mediated by different neuronal mechanisms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".