Effects of lorazepam and baclofen on short‐ and long‐latency afferent inhibition
Bibliographic record
Abstract
Key points Short‐latency afferent inhibition (SAI) is modulated by GABAA receptor activity, whereas the pharmacological origin of long‐latency afferent inhibition remains unknown. This is the first study to report that long‐latency afferent inhibition (LAI) is reduced by the GABAA positive allosteric modulator lorazepam, and that both SAI and LAI are not modulated by the GABAB agonist baclofen. These findings advance our understanding of the neural mechanisms underlying afferent inhibition. Abstract The afferent volley evoked by peripheral nerve stimulation has an inhibitory influence on transcranial magnetic stimulation induced motor evoked potentials. This phenomenon, known as afferent inhibition, occurs in two phases: short‐latency afferent inhibition (SAI) and long‐latency afferent inhibition (LAI). SAI exerts its inhibitory influence via cholinergic and GABAergic activity. The neurotransmitter receptors that mediate LAI remain unclear. The present study aimed to determine whether LAI is contributed by GABAA and/or GABAB receptor activity. In a double‐blinded, placebo‐controlled study, 2.5 mg of lorazepam (GABAA agonist), 20 mg of baclofen (GABAB agonist) and placebo were administered to 14 males (mean age 22.7 ± 1.9 years) in three separate sessions. SAI and LAI, evoked by stimulation of the median nerve and recorded from the first dorsal interosseous muscle, were quantified before and at the peak plasma concentration following drug ingestion. Results indicate that lorazepam reduced LAI by ∼40% and, in support of previous work, reduced SAI by ∼19%. However, neither SAI, nor LAI were altered by baclofen. In a follow‐up double‐blinded, placebo‐controlled study, 10 returning participants received placebo or 40 mg of baclofen (double the dosage used in Experiment 1). The results obtained indicate that SAI and LAI were unchanged by baclofen. This is the first study to show that LAI is modulated by GABAA receptor activity, similar to SAI, and that afferent inhibition does not appear to be a GABAB mediated process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".