Interactions between two different inhibitory systems in the human motor cortex
Bibliographic record
Abstract
Intracortical inhibition in the human motor cortex has been previously demonstrated using paired-pulse transcranial magnetic stimulation (TMS) protocols at short intervals (1-6 ms; short interval intracortical inhibition, SICI) with a subthreshold conditioning pulse preceding a suprathreshold test pulse, and at long intervals (50-200 ms; long interval intracortical inhibition, LICI) with suprathreshold conditioning and test pulses. We investigated whether different circuits mediate these inhibitory phenomena and how they interact. In nine healthy volunteers, we applied TMS to the motor cortex and recorded motor evoked potentials from the first dorsal interosseous muscle. With increasing test pulse strength, LICI decreases but SICI tends to increase. There was no correlation between the degree of SICI and LICI. We tested the interactions between SICI and LICI. SICI was reduced or eliminated in the presence of LICI. Loss of SICI was seen even with a conditioning stimulus too weak to induce significant LICI. Our findings demonstrate that different cell populations mediate SICI and LICI. The results are consistent with the hypothesis that LICI inhibits SICI through presynaptic GABAB receptors. Testing of SICI in the presence of LICI may be a non-invasive way of evaluating inhibitory interactions in the human motor cortex.
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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 | 0.002 |
| 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 | 0.001 |
| 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".