Investigating the effects of dopamine on short‐ and long‐latency afferent inhibition
Bibliographic record
Abstract
Short- and long-latency afferent inhibition (SAI and LAI respectively) are phenomenon whereby the motor evoked potential induced by transcranial magnetic stimulation (TMS) is inhibited by a sensory afferent volley consequent to nerve stimulation. It remains unclear whether dopamine participates in the genesis or modulation of SAI and LAI. The present study aimed to determine if SAI and LAI are modulated by levodopa (l-DOPA). In this placebo-controlled, double-anonymized study Apo-Levocarb (100 mg l-DOPA in combination with 25 mg carbidopa) and a placebo were administered to 32 adult males (mean age 24 ± 3 years) in two separate sessions. SAI and LAI were evoked by stimulating the median nerve and delivering single-pulse TMS over the motor hotspot corresponding to the first dorsal interosseous muscle of the right hand. SAI and LAI were quantified before and 1 h following ingestion of drug or placebo corresponding to the peak plasma concentration of Apo-Levocarb. The results indicate that Apo-Levocarb increases SAI and does not significantly alter LAI. These findings support literature demonstrating increased SAI following exogenous dopamine administration in neurodegenerative disorders. KEY POINTS: Short- and long-latency afferent inhibition (SAI and LAI respectively) are measures of corticospinal excitability evoked using transcranial magnetic stimulation. SAI and LAI are reduced in conditions such as Parkinson's disease which suggests dopamine may be involved in the mechanism of afferent inhibition. 125 mg of Apo-Levocarb (100 mg dopamine) increases SAI but not LAI. This study increases our understanding of the pharmacological mechanism of SAI and LAI.
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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 | 0.000 |
| 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) | 0.001 | 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".