Selective Effects of Baclofen on Use-Dependent Modulation of GABAB Inhibition after Tetraplegia
Bibliographic record
Abstract
Baclofen is a GABAB receptor agonist commonly used to relief spasticity related to motor disorders. The effects of baclofen on voluntary motor output are limited and not yet understood. Using noninvasive transcranial magnetic and electrical stimulation techniques, we examined electrophysiological measures probably involving GABAB (long-interval intracortical inhibition and the cortical silent period) and GABAA (short-interval intracortical inhibition) receptors, which are inhibitory effects mediated by subcortical and cortical mechanisms. We demonstrate increased active long-interval intracortical inhibition and prolonged cortical silent period during voluntary activity of an intrinsic finger muscle in humans with chronic incomplete cervical spinal cord injury (SCI) compared with age-matched controls, whereas resting long-interval intracortical inhibition was unchanged. However, long-term (~6 years) use of baclofen decreased active long-interval intracortical inhibition to similar levels as controls but did not affect the duration of the cortical silent period. We found a correlation between signs of spasticity and long-interval intracortical inhibition in patients with SCI. Short-interval intracortical inhibition was decreased during voluntary contraction compared with rest but there was no effect of SCI or baclofen use. Together, these results demonstrate that baclofen selectively maintains use-dependent modulation of largely subcortical but not cortical GABAB neuronal pathways after human SCI. Thus, cortical GABA(B) circuits may be less sensitive to baclofen than spinal GABAB circuits. This may contribute to the limited effects of baclofen on voluntary motor output in subjects with motor disorders affected by spasticity.
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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) | 0.001 | 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) | 0.001 | 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".