Short- and long-latency interhemispheric inhibitions are additive in human motor cortex
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) of the human primary motor cortex (M1) at suprathreshold strength results in inhibition of M1 in the opposite hemisphere, a process termed interhemispheric inhibition (IHI). Two phases of IHI, termed short-latency interhemispheric inhibition (SIHI) and long-latency interhemispheric inhibition (LIHI), involving separate neural circuits, have been identified. In this study we evaluated how these two inhibitory processes interact with each other. We studied 10 healthy right-handed subjects. A test stimulus (TS) was delivered to the left M1, and motor evoked potentials (MEPs) were recorded from the right first dorsal interosseous (FDI) muscle. Contralateral conditioning stimuli (CCS) were applied to the right M1 either 10 ms or 50 ms prior to the TS, inducing SIHI and LIHI, respectively, in the left M1. The effects of SIHI and LIHI alone, and SIHI and LIHI delivered together, were compared. The TS was adjusted to produce 1-mV or 0.5-mV MEPs when applied alone or after CCS. SIHI and LIHI were found to be additive when delivered together, irrespective of the strength of the TS. The interactions were affected neither by varying the strength of the conditioning stimulus producing SIHI nor by altering the current direction of the TS. Small or opposing interactions, however, may not have been detected. These results support previous findings suggesting that SIHI and LIHI act through different neural circuits. Such inhibitory processes may be used individually or additively during motor tasks and should be studied as separate processes in functional studies.
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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".