Short-Term Plasticity in a Monosynaptic Reflex Pathway to Forearm Muscles after Continuous Robot-Assisted Passive Stepping
Bibliographic record
Abstract
Both active and passive rhythmic limb movements reduce the amplitude of spinal cord Hoffmann (H-) reflexes in muscles of moving and distant limbs. This could have clinical utility in remote modulation of the pathologically hyperactive reflexes found in spasticity after stroke or spinal cord injury. However, such clinical translation is currently hampered by a lack of critical information regarding the minimum or effective duration of passive movement needed for modulating spinal cord excitability. We therefore investigated the H-reflex modulation in the flexor carpi radialis (FCR) muscle during and after various durations (5, 10, 15, and 30 min) of passive stepping in 11 neurologically normal subjects. Passive stepping was performed by a robotic gait trainer system (Lokomat(®)) while a single pulse of electrical stimulation to the median nerve elicited H-reflexes in the FCR. The amplitude of the FCR H-reflex was significantly suppressed during passive stepping. Although 30 min of passive stepping was sufficient to elicit a persistent H-reflex suppression that lasted up to 15 min, 5 min of passive stepping was not. The duration of H-reflex suppression correlated with that of the stepping. These findings suggest that the accumulation of stepping-related afferent feedback from the leg plays a role in generating short-term interlimb plasticity in the circuitry of the FCR H-reflex.
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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".